HEALTH MONITORING FOR DAMAGE INITIATION & PROGRESSION DURING MECHANICAL SHOCK IN ELECTRONIC ASSEMBLIES Except where reference is made to the work of others, the work described in this thesis is my own work or was done in collaboration with my advisory committee. This thesis does not include proprietary or classified information. _____________________________________________ Prakriti Choudhary Certificate of Approval: ______________________________ ______________________________ Robert L. Jackson Pradeep Lall, Chair Assistant Professor Thomas Walter Professor Mechanical Engineering Mechanical Engineering ______________________________ ______________________________ John L. Evans Joe F. Pittman Associate Professor Interim Dean Industrial & Systems Engineering Graduate School HEALTH MONITORING FOR DAMAGE INITIATION & PROGRESSION DURING MECHANICAL SHOCK IN ELECTRONIC ASSEMBLIES Prakriti Choudhary A Thesis Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Master of Science Auburn, Alabama May 10, 2007 iii HEALTH MONITORING FOR DAMAGE INITIATION & PROGRESSION DURING MECHANICAL SHOCK IN ELECTRONIC ASSEMBLIES Prakriti Choudhary Permission is granted to Auburn University to make copies of this thesis at its discretion, upon the request of the individuals or institutions and at their expense. The author reserves all publication rights. ______________________________ Signature of Author ______________________________ Date of Graduation iv VITA Prakriti Choudhary, daughter of Mr. Subhash and Dr. Veena Choudhary, was born on May 15, 1983 in New Delhi, India. Prakriti graduated with her Bachelors in Electrical Engineering from Delhi College of Engineering, New Delhi, India. During her bachelors she did internships at Lund University, Lund, Sweden and at the Dresden Design Centre (DDC), the R&D centre for Advanced Micro Devices (AMD) at Dresden, Germany. In the pursuit of enhancing her academic qualification she joined the M.S. Program at Auburn University in the Department of Mechanical Engineering in Fall 2004. During the M.S. program at Auburn University, she has worked under the guidance of Professor Pradeep Lall, in the Department of Mechanical Engineering and the Center for Advanced Vehicle Electronics (CAVE), as a Graduate Research Assistant in the area of reliability of electronic packages in a drop and shock environment. v THESIS ABSTRACT HEALTH MONITORING FOR DAMAGE INITIATION & PROGRESSION DURING MECHANICAL SHOCK IN ELECTRONIC ASSEMBLIES Prakriti Choudhary Master of Science, May 5, 2007 (B.E.E., Delhi College of Engineering, Delhi, India, 2004) 205 Typed Pages Directed by Pradeep Lall Electronic products may be subjected to shock and vibration during shipping, normal usage and accidental drop. Highstrain rate transient bending produced by such loads may result in failure of fine-pitch electronics. Current experimental techniques rely on electrical resistance for determination of failure. Significant advantage can be gained by prior knowledge of impending failure for applications where the consequences of system-failure may be catastrophic. This thesis focuses on an alternate approach to damage-quantification in electronic assemblies subjected to shock and vibration, without testing for electrical continuity. The proposed approach can be extended to monitor product-level damage. Statistical pattern recognition and leading indicators of shock-damage have been used to study the damage initiation and progression in shock and drop of electronic assemblies. vi Closed-form models have been developed for the eigen-frequencies and mode-shapes of electronic assemblies with various boundary conditions and component placement configurations. Model predictions have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock- damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response have been quantified. A damage index for Experimental Damage Monitoring has been developed using the failure indicators. The above damage monitoring approach is not based on electrical continuity and hence can be applied to any electronic assembly structure irrespective of the interconnections. The damage index developed provides parametric damage progression data, thus removing the limitation of current failure testing, where the damage progression can not be monitored. Hence the proposed method does not require the assumption that the failure occurs abruptly after some number of drops and can be extended to product level drops. vii ACKNOWLEDGEMENTS The author acknowledges and extends gratitude for financial support received from the National Science Foundation. Many thanks are due to the author?s advisor Prof. Pradeep Lall, and other committee members for their invaluable guidance and help during the course of this study. Deepest gratitude are also due to the author?s parents, Mr. Subhash Chowdhury, Dr. Veena Choudhary and brother Ankush Chowdhury for being constant source of inspiration and motivation, and to friends, Sameep Gupte, Dhananjay Panchagade and all other colleagues and friends whose names are not mentioned, for their priceless love and support. viii Style manual or journal used Guide to Preparation and Submission of Theses and Dissertations Computer software used Microsoft Office 2003 ix TABLE OF CONTENTS LIST OF FIGURES xiii LIST OF TABLES xviii CHAPTER 1 INTRODUCTION 1 1.1 Statistical Pattern Recognition 1 1.2 Health Monitoring 2 1.3 Current Testing Techniques 4 1.4 Closed Form Models 4 CHAPTER 2 LITERATURE REVIEW 6 2.1 Experimental Techniques 7 2.2 Statistical Pattern Recognition 9 2.3 Closed- Form Analytical Models 10 CHAPTER 3 STATISTICAL PATTERN RECOGNITION 13 3.1 Wavelet Transforms 14 3.1.1 Daubechies Wavelet 16 3.2 Wavelet Packet Approach 22 x 3.3 Distance Based Similarity 30 3.3.1 Euclidean Distance 30 3.3.2 Mahalanobis Distance Approach 31 CHAPTER 4 FAST FOURIER TRANSFORM & TIME FREQUENCY ANALYSIS 33 4.1 Fourier Tansforms 33 4.1.1 Discrete Fourier Transform 34 4.1.2 The Radix-2 FFT Algorithm 35 4.1.3 FFT Frequency Bands 41 4.2 Time Frequency Analysis 45 4.3 Linear Time Frequency Transforms 45 4.3.1 Short Time Fourier Transform 46 4.3.2 Continuous Wavelet Transform 47 4.3.3 Gabor Expansion 48 4.4 Quadratic Time Frequency Transforms 49 4.4.1 Wigner-Ville Distribution 49 4.4.2 Cohen Class of Transforms 52 4.4.3 Reduced Interference Distributions 53 4.5 Time Frequency Moments 57 4.6 Confidence Value Computation 61 4.6.1 Testing Hypothesis 61 CHAPTER 5 CLOSED FORM ANALYTICAL MODELS 63 xi 5.1 Derivation of the Lagrangian Functional 63 5.1.1 Development of the Virtual Strain energy 65 5.1.2 Development of the Virtual Kinetic Energy 67 5.1.3 Development of the Virtual Potential Energy 68 5.2 Development of Governing Differential Equation 68 5.2.1 Isotropic plates 70 5.2.2 Orthotropic plates 73 5.3 Plate Functional Derivation using Plate Strips 74 5.3.1 Plate Strip Displacement Function 74 5.3.2 For Simple-Simple plate strip 76 5.3.3 Free-Free plate strip 78 5.3.4 For a Clamped-Free Strip 81 5.3.5 For Clamped-Clamped plate strip 84 5.4 Application of Ritz Method 86 5.4.1 Completely Free (FFFF) Plate 87 5.5 Point Mass Components on the PCA 95 5.5.1 Eigenvalue Equation of a Constrained Plate 96 CHAPTER 6 APPLICATION AND VALIDATION OF PREDICTIVE MODEL 100 6.1 Development of Training Signal and High-Speed Measurement Transient Dynamic Response 100 6.2 Training of the Predictive Model 108 xii 6.3 Closed Form Model Results 119 6.3.1 CFFF to FFFF Boundary Condition change with change in aspect ratio 119 6.3.2 Point Mass Fall off from Assembly corresponding to Package Falloff 123 6.4 Model Based Correlation of Damage 128 6.4.1 Solder Ball Cracking and Failure 131 6.4.2 Chip Failure 138 6.4.3 Chip Delamination 138 6.4.4 Package Fall Off 145 6.5 Experimental Validation 145 6.6 Solder Joint Built in Reliability Test 157 CHAPTER 7 SUMMARY & CONCLUSIONS 162 BIBLIOGRAPHY 165 xiii LIST OF FIGURES Figure 1 A N th Level Wavelet Decomposition Structure. 16 Figure 2 The Daubechies-6 Wavelet. 20 Figure 3 The Scaling Function of a Daubechies 6 Wavelet. 20 Figure 4 Frequency Response of the Low-Pass Filter. 21 Figure 5 Frequency Response of the High-Pass Filter. 21 Figure 6 Wavelet Packet decomposition structure for Level three decomposition. 23 Figure 7 First six wavelet packets for a DB6 filter packet decomposition. 26 Figure 8 Transient Strain-History at Location of CSP during Drop-Event. 28 Figure 9 Wavelet Packet Energy Feature Vector. 29 Figure 10 Mahalanobis distance Feature Vector. 32 Figure 11 Fast Fourier Transform decimation based on Decimation in time algorithm. 37 Figure 12 A basic FFT butterfly structure used to combine the decimated signal to obtain the frequency spectrum. 40 Figure 13 Example of the Structural Combination of the decimated signal to produce the frequency spectrum. 40 Figure 14 : Transient Strain-History at Location of CSP during Drop-Event. 42 Figure 15 : FFT Frequency Band Energy Feature Vector. 43 Figure 16 The Receptance Plot obtained by the Modal Analysis of the TABGA Board. 44 Figure 17 The Mode shapes and natural frequencies of vibration of the Board. 44 xiv Figure 18 Time Frequency Analysis Techniques. 45 Figure 19 Time Frequency Distribution for a Transient Strain signal. 59 Figure 20 Time Moment Feature Vector for a Transient Strain Signal. 60 Figure 21 Frequency Moment Feature Vector for a Transient Strain Signal. 60 Figure 22 Modeshape Correlation of a Completely Free plate with [Leissa 1969]. 94 Figure 23 Point Mass representation of the Electronic Assembly. 99 Figure 24 Interconnect array configuration for Test Vehicles. 101 Figure 25: Interconnect array configuration for 95.5Sn4.0Ag0.5Cu and 63Sn37Pb Test Vehicles. 103 Figure 26 Measurement of Velocity, Acceleration, and Relative Displacement During Impact. 106 Figure 27 Relative Displacement and Strain Measurement in Horizontal Orientation. 106 Figure 28 Transient Strain-History at Location of CSP during Drop-Event. 107 Figure 29 Strain data for Repeatable Drops of an electronic Assembly. 109 Figure 30 Repeatable Feature Signatures obtained using Wavelet Packet Energy Vectors. 110 Figure 31 Confidence Values obtained by applying Wavelet Packet Energy Approach to Repeatable Drops (No Failure). 111 Figure 32 Repeatable Feature Signatures obtained using Mahalanobis Distance Vectors. 112 Figure 33 Confidence Values obtained by applying Mahalanobis Distance computation to Repeatable Drops (No Failure). 113 xv Figure 34 Repeatable Feature Signatures obtained using FFT Frequency Bands Energy Vectors. 114 Figure 35 Confidence Values obtained by applying FFT Frequency Band Energy computation to Repeatable Drops (No Failure). 115 Figure 36 Repeatable Feature Signatures obtained using Time Moment Vectors. 116 Figure 37 Repeatable Feature Signatures obtained using Frequency Moment Vectors. 117 Figure 38 Confidence Values obtained by applying Time Frequency Analysis to Repeatable Drops (No Failure). 118 Figure 39 Confidence Value Degradation with Change in Aspect Ratio for Mode 1. 121 Figure 40 Confidence Value Degradation with Change in Aspect Ratio for Mode 2. 122 Figure 41 Point Mass Closed Form Model and Numbering of Location of Packages. 124 Figure 42 Degradation in Confidence Value with respect to Location of Package Fall off. 125 Figure 43 Degradation in Confidence Value with Package Fall off from Location 1. 126 Figure 44 Effect of Package Fall off on Modeshape of Assembly. 127 Figure 45 Package with Solder Beam Array. 129 Figure 46 Solder Bam Array modeled to represent Solder Balls. 129 Figure 47 Vertical Drop Model developed for the Study. 130 Figure 48 Horizontal Drop Model developed for the Study. 130 Figure 49 Model Configurations for Correlation of Interconnect Failure to Confidence Value Degradation. 132 Figure 50 Model Configurations for Correlation of Interconnect Damage to Confidence Value Degradation. 133 xvi Figure 51 Confidence Value degradation in Transient PCB Strain with Solder Ball Failure for Vertical Drop. 134 Figure 52 Confidence Value degradation in Transient PCB Strain with Solder Ball Failure for Horizontal Drop. 135 Figure 53 Confidence Value degradation in Transient PCB Strain with Solder Ball Damage for Vertical Drop. 136 Figure 54 Confidence Value degradation in Transient PCB Strain with Solder Ball Damage for Horizontal Drop. 137 Figure 55 Model configuration for Chip Fracture (cracking). 139 Figure 56 Confidence Value degradation in Transient PCB Strain with Chip Failure for Vertical drop orientation. 140 Figure 57 Confidence Value degradation in Transient PCB Strain with Chip Failure for Horizontal drop orientation. 141 Figure 58 Model configuration for Chip Delamination. 142 Figure 59 Confidence Value degradation in Transient PCB Strain with Chip Delamination for Vertical drop orientation. 143 Figure 60 Confidence Value degradation in Transient PCB Strain with Chip Delamination for Horizontal drop orientation. 144 Figure 61 Model configuration for loss of package from assembly (a) Vertical Drop (b) Horizontal Drop. 146 Figure 62 Confidence Value degradation in Transient PCB Strain with Package Loss for Vertical drop orientation. 147 xvii Figure 63 Confidence Value degradation in Transient PCB Strain with Package Loss for Horizontal drop orientation. 148 Figure 64 Wavelet Packet Energy Feature Vector used in Failure Classification. 150 Figure 65 Mahalanobis Distance Feature Vector used for Failure Classification. 151 Figure 66 FFT Frequency Band Energy Feature Vector used for Failure Classification. 152 Figure 67 Time Moment Feature Vector used for Failure Classification. 153 Figure 68 Frequency Moment Feature Vector used for Failure Classification. 154 Figure 69 Confidence value degradation showing progressive damage with the Drops. 155 Figure 70 Confidence value degradation showing progressive damage with the Drops. 156 Figure 71 Solder Joint Built in Reliability test Circuit Design. 159 Figure 72 Voltage Characteristics obtained due to variation in Solder interconnect resistance due to heath degradation. 160 Figure 73 Damage Detection using a Solder Joint Built in Reliability Test (SJ-BIRT). 161 Figure 74 The Degradation in Confidence Value relative to Damage occurrence in Assembly. 164 xviii LIST OF TABLES Table 1-1 Statistical Pattern Recognition Techniques and Applications. 3 Table 4-1 Kernels applied during Time Frequency analysis [Cohen 1995]. 54 Table 4-2 List of Common Correspondence Functions. 58 Table 5-1 Roots of the transcendental equation for a simply supported-simply supported plate. 78 Table 5-2 Roots of the transcendental equation of a Free-Free plate. 80 Table 5-3 Values of ? r for a free-free plate. 81 Table 5-4 Roots of the transcendental equation of a Clamped-Free plate. 83 Table 5-5 Values of ? r for a Clamped-Free plate. 84 Table 5-6 Roots of the transcendental equation of a Clamped-Clamped plate. 86 Table 5-7 Values of ? r for a Clamped-Clamped plate. 86 Table 6-1: Test Vehicles. 102 Table 6-2: Test Vehicles. 104 1 CHAPTER 1 INTRODUCTION Electronic packaging refers to an electromechanical platform which is both economical and manufacturable and provides protection to the delicate silicon die [Gilleo 2002]. It provides the geometric translations which is required for the compatible interface between the electronic device and the next system level. The package provides protection from the external environment, external loads and stress by enclosing the silicon die in electrically insulative materials and from moisture by hermetically sealed packages like metal vacuum-sealed packages and gas-impervious ceramic packages. Some other major functions of electronic packages are heat dissipation, signal distribution and power distribution. The advances in semiconductor fabrication and packaging techniques have caused an increase in the interconnect densities of the packages on the PCB and also an increase in the reliability considerations for the electronic devices. Damage due to shock and vibration on portable products may manifest in solder interconnects and copper traces and cause failure of the packages. The damage may be due to sudden overstress or from cumulative stress. 1.1 Statistical Pattern Recognition Statistical Pattern Recognition refers to the study of algorithms that recognize patterns in data. The above research area also contains various sub disciplines like 2 discriminant analysis, feature extraction, error estimation, and cluster analysis. Some important application areas of statistical pattern recognition are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. The various methods applied for statistical pattern recognition and their applications are summarized in Table 1-1. Though research and development in the field of statistical pattern recognition has been going on for the past 50 years its application to reliability studies of electronic assemblies is new. Currently statistical pattern recognition is being employed in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, artificial intelligence, computer vision and remote sensing [Jain et.al.2000]. 1.2 Health Monitoring Structural health monitoring, i.e. the process of establishing knowledge of the current condition of a structure has found application in various fields, like shaft crack detection [Lebold, et. al. 2004, Gyekenyesi, et. al. 2003] and aircraft maintenance [Hedley, et. al. 2004, Hickman, et. al. 1991, Castanien, et. al. 1996]. This method also finds application in performance assessment of Machinery systems [Lee 1995, Chuang, et. al. 2004, Wegerich 2003]. While, structural health monitoring is popularly used in various fields, its application to the field of reliability of electronic structures is new. The relevant features, like vibration, temperature etc. are extracted from strategically placed sensors on the machine structure, and the algorithms developed for performance assessment of a system are applied. Experience in other applications indicates that structural health monitoring produces gains in the performance and cost-effective 3 Table 1-1 Statistical Pattern Recognition Techniques and Applications. Method for Statistical Pattern Recognition Application Reference Neural Networks (Probabilistic and Artificial combined with fuzzy logic) Fault in Gas Turbine engines [Atlas 1996, Sick 1998, Chuang 2004] Hidden Markov Model Speech Recognition Machine Tool Wear [Litao 2001, Heck 1991] Multivariate Similarity Modeling Machinery Health Monitoring [Wegerich 2003] Auto Regression models Machinery Health Monitoring [Logan 2001, Shao 2000, Lei 2003, Casoetto 2003, Yan 2004, Engel 2000] Wavelet Packet Approach Tool wear [Yan 2003] FFT based frequency domain analysis Machine monitoring [Yuan 2004] Time series methods (Time-frequency moments) Machine Tool Monitoring [Zheng 1992, Djurdjanovic 2002] Statistical Data Comparison (Kurtosis, Crest factor etc.) Railway Bearing Diagnostics [National Research Council Canada 1999] 4 maintenance of high-value assets such as aircrafts, civil infrastructure and maritime vessels. Structural health monitoring systems help in reducing down-time and eliminating component teardown inspections, thus reducing the risk of failure during operation. 1.3 Current Testing Techniques Currently the main reliability tests performed on electronic assemblies undergoing drop and shock are the JEDEC drop test [Lall, et. al. 2005], Shear testing [Hanabe, et. al. 2004] and ball pull testing [Newman 2005]. The JEDEC drop test is based on the JEDEC test standards, and studies the affect of drop and shock experimentally on Test boards. These tests are limited to board level drops with the packages on the board connected in a daisy chain as the experimental techniques relies on measurement of electrical resistance for determination of failure. Ball-pull testing and shear testing has also been applied to test structures to study the reliability of electronic assemblies in drop and shock environments. These tests quantitatively study the impact toughness of solder joints by means of various tests including the Charpy test [Date, et. al. 2004], Shear test, and the package to board interconnection shear strength (PBISS) technique [Hanabe, et. al. 2004]. The above mentioned test procedures cannot monitor the damage occurring during shock and drop, hence they cannot be used for health monitoring of electronic assemblies. 1.4 Closed Form Models The electronic assembly is modeled as a rectangular board with point masses on it, representing the PCB with the packages attached to it. Various kinds of boundary conditions have been studied, determined by the packaging of the assembly at the product 5 level. Hence a press fit edge of a PCB is modeled as a clamped edge condition, while a configuration with screws attaching the PCB to the casing is modeled as a plate having point supports. In this paper, the JEDEC drop test assembly has been modeled as a rectangular plate on rigid point supports, with packages being modeled as attached masses on the PCB. The vibrational frequency and mode shapes have been correlated with FEM models developed for JEDEC drop testing and also with experimental data obtained during the tests. Various case studies of failure occurring due to change in effective attributes of the assembly have been discussed and damage monitoring done to show damage progression. 6 CHAPTER 2 LITERATURE REVIEW Thermal loading is generally considered the major cause of failure in electronic devices. The mismatch in the coefficient of thermal expansions (CTE) of the various materials in the package causes various types of failures, some of which are solder joint cracking, chip delamination, and silicon chip cracking. However many of the electronic devices are subjected to extreme environments where the devices have to sustain high amounts of vibrations and shock. The U.S. Air Force estimates that vibration and shock cause 20 percent of the mechanical failures in airborne electronics [Zhao et.al. 2000]. The nonlinear stress-strain behavior of solder joints under vibration is still not clear, and the role of vibration in the life of solder joints has not been studied sufficiently. Electronic assemblies are susceptible to failure due to shock and drop as the electronic products may be subjected to drop and shock due to mishandling during transportation or during normal usage. Some specific electronic products such as portable communication and computing products which contain fine-pitch ball-grid arrays, and quad-flat no-lead packages, are very susceptible to shock-related impact damage. Electronic assemblies used in military applications are repetitively subjected to extreme shock due to various factors such as artillery fire but require reliable functioning even after such high impacts [Lall et.al 2005]. 7 2.1 Experimental Techniques Several experimental tests are performed to study the reliability of electronic devices that are designed to operate in specific environmental conditions. These tests are designed and performed to study the effect of the various environmental parameters such as temperature, humidity, and vibration on the packages. Researchers [Silverman 1998, Chengalva 2004] have applied various test conditions such as the accelerated thermal cycling, thermal shock, HAST (highly accelerated stress test) and vibration tests, to analyze the reliability of the packages for various applications. The current drop and shock testing of electronic assemblies fall into mainly two categories, the constrained drop testing and the unconstrained or free drop testing. One of the test standard defined for the constrained drop testing is the JEDEC test standard [2003] which has found vast application in the comparative drop performance assessment of surface mount electronic components found in compact handheld electronic products. The JEDEC standard test are generally performed at the component level, thus the primary goal of the JEDEC test is to provide a reproducible assessment of drop performance by standardizing the test board and the test methodology. The limitation of the JEDEC testing lays in the correlation of performance at product level with the test results. The reliability failures in product assemblies are pertinent on various affecting factors such as the product casing, and the drop orientation which might not be perpendicular to the board surface [Lim 2002]. The previous research efforts [Tian 2003, Lall 2004] on constrained drops for edge-based drop orientation have shown repeatable drops. Unconstrained or free drop testing has been proposed by the use of high speed 8 photography [Goyal 2000] with the limitation of difficulty in getting repeatable drops due to edge clattering i.e. one corner of the product touches the ground first and the other corner rebounds repeatedly. Both the constrained and unconstrained testing of the electronic assemblies requires the packages on the test board to be connected in a daisy chain so as to facilitate failure monitoring. These tests are limited to board level drops with the packages on the board connected in a daisy chain as the experimental techniques relies on measurement of electrical resistance for determination of failure Mechanical tests performed to test solder joints are the shear test, the pull test and the peel-off test [Nishiura et.al 2002, Jeon et.al. 2002]. These tests are utilized to perform bulk or bond testing of the joints as compared to impact testing. The test speeds for these tests are less than 10mm/s, which is very low when compared to the strain rates applied to solder joints during drop and impact. Another test suggested and applied to the mechanical testing of solder joints during shock and drop is the miniature Charpy test [Morita et.al. 2002] where the shear rate is approximately 1m/s [Date et.al. 2004]. The quantification of the shear strength and the effect of pad finish in Chip Scale Packages is performed by Shear testing [Canamulla et.al. 2003] and ball pull testing [Newman 2005] performed at a strain rate of 100?m/sec. As the strain rate in shear tests are low, the shear strength behavior shown by solders due to slow deformation is not applicable to the study of electronics in shock and drop [Hanabe et.al. 2004]. 9 The above mentioned test procedures cannot monitor the damage occurring during shock and drop, hence they cannot be used for health monitoring of electronic assemblies. 2.2 Statistical Pattern Recognition Statistical Pattern Recognition refers to the study of algorithms that recognize patterns in data and contains various sub disciplines like discriminant analysis, feature extraction, error estimation, and cluster analysis. Some important application areas of statistical pattern recognition are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. Statistical pattern recognition has been developed using several methods and applied to a plethora of applications including neural networks applied to faults in gas turbine engines [Atlas 1996, Sick 1998, Chuang 2004], Hidden Markov models applied to speech recognition and machine tool wear [Wang 2002, Heck 1991], multivariate similarity models applied to machine health monitoring [Wegerich 2003], auto-regression models applied to machine health monitoring [Logan 2003, Shao 2000, Lei 2003, Casoetto 2003, Yan 2005, Engel 2000], wavelet packet approach applied to tool wear [Yan 2004], FFT based frequency-domain analysis applied to machine monitoring [Yuan 2004], time-series methods applied to machine tool monitoring [Zheng 1992, Djurdjanovic 2002], and statistical data comparison applied to railway bearing diagnostics [National Research Council Canada 1999]. Application of statistical pattern recognition to health monitoring of electronic assemblies subjected to shock and vibration environments is new. 10 2.3 Closed- Form Analytical Models A closed-form modeling approach has been used to analyze the dynamic behavior of printed circuit assembly with component masses. The Lagrangian Functional for a rectangular printed-circuit assembly is, ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? ?+ ? ? ? ? + ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? = A 2 o 2 o 2 2 ooo 2 o 2 2 o 2 2 o 2 2 2 o 2 2 2 o 2 dxdy y W x W I)W(I 2 1 qW yx W ?)2(1 y W x W 2? y W x W 2 D L && & where, W 0 is the Mode shape Functional, D is the Flexural Rigidity and depends on E which is the elastic modulus, ? which is the Poisson?s Ratio, and h which is the thickness of the printed-circuit board. The area over which the integration has to be performed is defined by, a which is the length of the PCB, and b which is the width of the PCB. The distributed loads on the PCB is represented by q, ? is the density of the PCB, and I o and I 2 are the mass moments-of-inertia. Work on the analytical solution for the free vibration of rectangular plates having various boundary conditions can be found easily in previous publications [Leissa 1969, Young 1950]. There are various techniques that have been utilized during the solution of the governing differential equation of a rectangular plate, () ? ? ? ? ? ? ? ? ? ? + ? ? +?= ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? ? ? ? ? ? ? + ?? ? ++ ? ? 2 o 2 2 o 2 2oo o yy o xy o xy o xx 4 o 4 22 22 o 4 1266 4 o 4 11 y W x W IWIq y W N x W N y y W N x W N x y W D yx W 2DD2 x W D &&&& && 11 The first comprehensive collection of solutions for rectangular plates was presented by Warburton [Warburton 1954, Leissa 1969, ]. The Rayleigh method was used to analyze the deflection and frequency data for the free vibration of rectangular plates. The deflection function was defined as the product of the beam deflection functions, where the beam functions represented the fundamental mode shapes of the beams having the boundary conditions of the plate. )y(Y)x(X)y,x(W = The above method satisfies all the boundary conditions for the plate, except the free edge condition, where the shear condition on the plate is approximately satisfied. Janich [Leissa 1969] also suggested a comprehensive set of solutions for the free vibration of rectangular plates having different boundary conditions. He obtained the fundamental frequencies of vibration for 18 sets of boundary conditions. The method suggested also used the Rayleigh Ritz solution and utilized simple trigonometric functions to represent the beam functions. These two methods yield the upper limits of the frequency and mode shape values, but are not very suitable to study the vibrations of plates with certain boundary conditions like a completely free plate. The results for higher mode shapes also decrease in accuracy with the mode number. Several other studies and methods have been developed for the analytical solutions of rectangular plates based on the specific boundary conditions of the plate. Some of the previous solutions specifically solved particular boundary conditions. Gorman also developed a solution for the complete set of boundary conditions and 12 developed the displacement functionals based on the antisymmetric and the symmetric modes of the rectangular plate [Gorman 1982]. The Rayleigh-Ritz method was also employed by Young to solve the free vibration of rectangular plates with various boundary conditions [Young 1950]. The study also provides the upper limit of the vibration frequencies of a rectangular plate but yields satisfactory results for studying various problems in equilibrium, buckling and vibration. This study employs the Rayleigh Ritz method to solve for the vibrational frequencies and modeshapes of an electronic assembly. The electronic components placed on the PCB surface have been modeled as point masses on a rectangular plate while developing the closed form analytical models. The normal vibrations of a rectangular plate with point masses attached on the surface has been studied in various works [Das et.al. 1963, Chintakindi 1964, Shah et.al. 1969, G?rg?ze 1984, and Ingber et.al. 1992] but have focused on simply supported plates and beams. The representation of electronic assemblies as rectangular plates with point masses attached is new. The analytical solution developed using the Rayleigh Ritz method for the free vibration of rectangular plates can be modified to obtain the vibration frequencies and modeshapes of plates with attached masses [Wu et.al. 1997]. This method allows the use of previously developed models for the free vibration of free rectangular plates having various boundary conditions and hence is applicable to all the developed boundary conditions. 13 CHAPTER 3 STATISTICAL PATTERN RECOGNITION Statistical Pattern Recognition is defined as a set of algorithms that recognize patterns in data and has found application in the areas of image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. The application of Statistical pattern recognition to the study of prognostics and damage monitoring in electronic assemblies undergoing a shock and drop event is new though several methods have been developed and applied to various applications. In this study statistical pattern recognition is used to study the degradation of reliability in electronic assemblies, due to shock and drop. The health monitoring of assemblies has been accomplished by monitoring the confidence values computed by applying statistical pattern recognition techniques to the transient-strain response, transient displacement-response, vibration mode shapes and frequencies of the electronic assembly under shock and drop. Correlation of structural response, damage proxies and underlying damage has been accomplished with closed-form models, explicit finite element models and validated with high-speed experimental data. In this chapter, two 14 statistical pattern recognition techniques, including the wavelet packet approach and the Mahalanobis distance approach have been investigated. 3.1 Wavelet Transforms Wavelets have been used in several areas including data and image processing [Martin 2001], geophysics [Kumar 1994], power signal studies [Santoso 1996], meteorological studies [Lau 1995], speech recognition [Favero 1994], medicine [Akay 1997], and motor vibration [Fu 2003, Yen 1999]. Wavelets based time-frequency analysis is specifically useful to analyze non-stationary signals. The wavelet transform is defined by dt s ut * ?f(t) s 1 su, ?f,s)Wf(u, ? ? ? ? ? ? ? ? +? ?? == where the base atom ?* is the complex conjugate of the wavelet function which is a zero average function, centered around zero with a finite energy. The function f(t) is termed as the mother wavelet and is decomposed into a set of basis functions called the wavelets with the variables s and u, representing the scale and translation factors respectively. The original signal is first passed through a half-band highpass filter g[n] and a lowpass filter h[n]. After the filtering, half of the samples are eliminated according to the Nyquist?s rule, since the signal now has a highest frequency of p/2 radians instead of p. The signal is therefore sub-sampled by 2, simply by discarding every other sample. 15 This constitutes one level of decomposition and can mathematically be expressed as follows: ? ? ??= ??= n low n high n]h[2ksignal[n][k]y n]g[2ksignal[n][k]y where y high [k] and y low [k] are the outputs of the highpass and lowpass filters, respectively, after subsampling by 2. However, the number of average number of data points out of the filter bank is the same as the number input, because the number is doubled by having two filters. Thus, no information is lost in the process and it is possible to completely recover the original signal. Aliasing occurring in one filter bank can be completely undone by using signal from the second bank. Further, the time resolution after the decomposition halves as the sub-sampling occurs. However this sub-sampling doubles the frequency resolution, as after decomposition the frequency band of the signal spans half the previous frequency band, effectively reducing the uncertainty in the frequency by half. At every level, the filtering and subsampling will result in half the number of samples (and hence half the time resolution) and half the frequency band spanned (and hence doubles the frequency resolution).The frequencies that are most prominent in the original signal will appear as high amplitudes in that region of the Wavelet transform signal that includes those particular frequencies. The time localization will have a resolution that depends on which level they appear. The wavelet decomposition of the signal is shown below in Figure 1. 16 Signal Approximation 1 Detail 1 Approximation 2 Detail 2 Detail N Approximation N Figure 1 A N th Level Wavelet Decomposition Structure. 3.1.1 Daubechies Wavelet The orthonormal expansion was developed to improve on the performance of the Fourier expansion and other classical expansions. The Fourier series expansion is not well localized in space and the Haar series used in the Haar wavelets is very well localized and hence limits the observation of the signal behavior in a given time interval. If the mother wavelet used in the wavelet transforms forms an orthonormal basis in )(L 2 ? , then the mother wavelet is capable of generating any function in )(L 2 ? [Benedetto et.al. 1994]. In the wavelet analysis performed in this study, the Daubechies wavelet has been chosen for analysis of transient dynamic signals mainly based on resemblance of the wavelet with the true signal. The Daubechies-wavelets are defined two functions, i.e. the scaling function ?(x), and the wavelet function ?(x). The Daubechies wavelet algorithm 17 uses overlapping windows, so the high frequency spectrum reflects all changes in the time series. Daubechies wavelet shifts its window by two elements at each step. However, the average and difference are calculated over four elements, so there are no "holes" unlike other wavelet transforms such as the Haar transform, which use a window which is two elements wide. With a two element wide window, if a big change takes place from an even value to an odd value, the change will not be reflected in the high frequency coefficients. The scaling function is the solution of the dilation equation, ? ? = ?(2?=)(? 1L 0u )ut)u(h2t where h(u) are a sequence of real or complex numbers called the scaling function coefficients, 2 represents the normalization of the scaling function with a scale of two, )t(? is normalized ? ? ?? =? 1dt)t( . The wavelet )t(? ?is defined in terms of the scaling function, ? ? = ?(2?=? 1L 0u )ut)u(g2)t( where the coefficients g(u) defines the scaling function. Building on the orthonormal basis from )t(? and )t(? ?by dilating and translating, the following functions are obtained, )ut2)t( s 2 s s u ?(2?=? 18 )ut2(2)t( and s 2 s s u ??=? where s is the dilation parameter and u is the translation parameter. The variables )u(gand)u(h are the filter coefficients of the Quadrature mirror filters H and G respectively [Walnut 2002]. The QMF H and G must satisfy the following properties. I * GG * HH and 0 * GH * HG == == where I is an identity matrix. To obtain QFM, the matrix computed using the filter coefficients h (u) and g (u) shown below must have a unitary value for a value of R?? [Daubechies 1988]. ? ? ? ? ? ? ? ? )?+?? )?+?? (m)(m (m)(m 11 00 ? ? ? = ? ? = ? =? =? 1L 0u in 1 1L 0u in 0 e]u[g)(m and e]u[h)(m where The advantages of the Daubechies wavelet are attributed to the property of vanishing moments exhibited by the Daubechies wavelet basis. The limitation of the Haar wavelet is attributed to the presence of jump discontinuities in the wavelets which cause 19 the Haar coefficients to decay poorly for smooth functions and also give poor signal reconstruction. All the wavelet functions satisfy the condition ? =? R 0dx)x( which represent the zeroth moment of )x(? , and hence this states that the zeroth moment is vanishing for all the wavelets. The m th moment of )x(? is given by ? <<? R m Nm0wheredx)x(x It has been shown that the greater the number of vanishing moments in a wavelet, the smoother is the wavelet [Walnut 2002]. The presence of a higher number of vanishing moments causes the wavelet functions to have fewer wavelet coefficients, as functions which are smooth require few wavelet coefficients to represent them accurately as compared to a non smooth function. A Daubechies wavelet of order N has N vanishing moments and is supported on the interval [0, 2N-1]. As the vanishing moments for the Daubechies wavelets are large in number hence they can better represent signals and do not suffer from jump discontinuities. The Daubechies wavelets show regularity which ensures the smoothness of the wavelet and the wavelet is compactly supported in [0,?N- 1], i.e. )t(? is zero outside [0,?N-1]. The Daubechies Wavelet also satisfies the admissibility condition which implies that the mean of the wavelet is zero and hence it represents oscillatory motion. The basic shape of a Daubechies wavelet (DB 6) used in this study and its scaling function has been shown below in Figure 2 and Figure 3 respectively. 20 Figure 2 The Daubechies-6 Wavelet. Figure 3 The Scaling Function of a Daubechies 6 Wavelet. 21 The strain signal obtained from the impact drop test has a sampling time of 0.2 to 0.4 s, i.e., a sampling frequency of 2.5 to 5 MHz. For the 2.5 MHz test data, in order to avoid aliasing during our analysis, we perform our transforms and calculations using the Nyquist frequency, which is half of the sampling frequency, i.e. 1.25 MHz. The Low- Pass and High-Pass filters used during the transform have a frequency response shown in Figure 4 and Figure 5. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -1500 -1000 -500 0 Normalized Frequency (?? rad/sample) P h ase ( d egr ees) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -300 -200 -100 0 100 Normalized Frequency (?? rad/sample) M a gni t ude ( dB ) Figure 4 Frequency Response of the Low-Pass Filter. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -600 -400 -200 0 Normalized Frequency (?? rad/sample) P h ase ( d egr ees) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -300 -200 -100 0 100 Normalized Frequency (?? rad/sample) M a gni t ude ( dB ) Figure 5 Frequency Response of the High-Pass Filter. 22 3.2 Wavelet Packet Approach The application of a pair of lowpass and highpass filters corresponds to decimating a signal into a low frequency approximation component and a high frequency detail component. In wavelet decomposition the low frequency component is further decimated while the high frequency component is not filtered. This causes some high frequency data loss and hence wavelet packets have been applied in this study. In wavelet packet decomposition we can decimate both the high frequency and the low frequency component. The wavelet filters are applied to the high frequency part at each level and the component is also decimated into a high frequency part and a low frequency part. This allows better frequency localization of the signals as the frequency space is divided into more number of tiles. The transform has been used to analyze transient strains signals at different frequency bands with different resolutions by decomposing the transient signal into a coarse approximation and detail information. The signal is decimated into different frequency bands by successively filtering the time domain signal using lowpass and highpass filters. The original stress signal is first passed through a halfband highpass filter g[n] and a lowpass filter h[n]. After the filtering, half of the samples are eliminated according to the Nyquist?s rule, since the signal now has a highest frequency of p/2 radians instead of p. The decimation structure of a signal based on a wavelet packet approach has been outlined in Figure 6. 23 Signal A 1 D 1 AA 2 DA 2 AD 2 DD 2 AAA 3 DAA 3 ADA 3 DDA 3 AAD 3 DAD 3 ADD 3 DDD 3 Figure 6 Wavelet Packet decomposition structure for Level three decomposition. 24 The signal is therefore subsampled by 2, simply by discarding every other sample. This constitutes one level of decomposition and can mathematically be expressed as follows: ? ? ??= ??= n low n high n]h[2ksignal[n][k]y n]g[2ksignal[n][k]y where y high [k] and y low [k] are the outputs of the highpass and lowpass filters, respectively, after subsampling by 2. The compression and de-noising in the wavelet packet transform is same as those for a wavelet transform framework. The only difference is that wavelet packets offer a more complex and flexible analysis, because in wavelet packet analysis, the details as well as the approximations are split. Wavelets have been shown to be useful for texture classification because of their finite length [Lee, et. al. 1995]. If an orthonormal basis is chosen, the wavelet coefficients are independent and possess the distinct features of the original signal. Wavelet packets can be described by the following collection of basis functions, () ( ) () ()? ?? ? = ? ? ? ? ? ? ? ? + ? ?? ? = ? ? ? ? ? ? ? ? m mx p 2 n W p 22lmg pl 2lx 1p 2 12n W m mx p 2 n W p 22lmh pl 2lx 1p 2 2n W where p is a scale index, l is a translation index, ?h? is a lowpass filter, and ?g? is a high- pass filter with k)h(l k 1)(g(k) ??= . The function (x) o W can be identified as the low- pass scaling function ? and (x) l W as the high-pass mother wavelet ?. A 2-D wavelet packet basis function is given by the product of two 1-D wavelet packet basis along the horizontal and vertical direction. 25 The corresponding 2-D filter coefficients have four groups, g(k)g(l)l)gg(k, g(k)h(l)l)gh(k, h(k)g(l)l)hg(k, h(k)h(l)l)hh(k, = = = = Wavelet packet basis functions have the properties of smoothness, number of vanishing moments, symmetry, good time and frequency localization, satisfy the admissibility condition and are absolutely square integrable functions. The discrete wavelets can be classified as non-orthogonal, biorthogonal or orthogonal wavelets. Non- orthogonal wavelets are linearly dependent and redundant frames. Orthogonal wavelets are linearly independent, complete and orthogonal. In the analysis of reliability of electronic assemblies, one of the most widely used wavelets constructed by Daubechies has been applied. The Daubechies wavelets are orthonormal, compactly supported, have maximum number of vanishing moments, and are reasonably smooth. The low-pass and band (high)?pass filter coefficients of the Daubechies wavelets satisfy the conditions of Orthogonality, Normality and Regularity. An entropy-based criterion is used to select the most suitable decomposition of a given signal. This implies that at each node of the decomposition tree, the information gained by performing a split is quantified. Simple and efficient algorithms exist for both wavelet packet decomposition and optimal decomposition selection. Adaptive filtering algorithms, allow the Wavelet Packet transform to include the "Best Level" and "Best Tree" features that optimize the decomposition both globally and with respect to each node. 26 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 024681012 ti -1.5 -1 -0.5 0 0.5 1 1.5 024681012 -1.5 -1 -0.5 0 0.5 1 1.5 2 024681012 -1.5 -1 -0.5 0 0.5 1 1.5 024681012 -2 -1.5 -1 -0.5 0 0.5 1 1.5 024681012 -1.5 -1 -0.5 0 0.5 1 1.5 024681012 Figure 7 First six wavelet packets for a DB6 filter packet decomposition. 27 For obtaining the optimal tree, a node is split into two nodes, if and only if the sum of entropy of the two nodes is lower than the sum of entropy of the initial node. After the wavelet packet transform, the wavelet packet energy is calculated at each node of the decomposition tree. An energy signature E n for each sequence of wavelet packet coefficients p kn, C for 1p ....40,1,2.....n ? = can be computed by using the formula ? = = N 1k 2 p n C 2 N 1 n E where N is the total number of points in the signal at a given node in the wavelet packet tree, p is the decomposition depth, and C i is the wavelet packet coefficients obtained during the wavelet packet transform at the particular node where energy is being calculated. The feature vector of length 1p 4 ? , is formed for the signal. The packet energies obtained from the wavelet packet decomposition of the various mode shapes and frequencies of vibration of the electronic assembly are the basis for the computation of confidence values for health monitoring. The Strain signal obtained from sensors placed on the electronic assembly as shown in Figure 8 is used to obtain the wavelet packet energy signature. The wavelet packet energy signature for a transient strain signal is shown in Figure 9. 28 Figure 8 Transient Strain-History at Location of CSP during Drop-Event. 29 Figure 9 Wavelet Packet Energy Feature Vector. 30 3.3 Distance Based Similarity The application of algorithms that automatically map data points known as the feature vectors to compute the degree of similarity between two objects is termed as a Similarity measure. The application of distance-based similarity utilizes the relative distance among data objects to perform similarity measures between the two given data objects. It has found application in various application such as Face recognition [Fraser et.al. 2003, Kamei 2002], Image analysis [Kato et.al. 1999, Kokare et.al. 2003], and Medical signals [Babiloni et.al. 2001, Momenan et.al. 1994]. The approach creates a data structure that allows the scalable retrieval of complex data required for patter recognition. The distance based similarity approach requires the distance function to be metric, hence requires the distance to satisfy the metric conditions. )Z,Y(D)Y,X(D)Z,D(X:Inequality Triangle )X,Y(D)Y,X(D:lSymmetrica 0)Y,X(D:Value Positive nnnnnn nnnn nn +? = ? 3.3.1 Euclidean Distance The mathematical definition of the Euclidean distance states that the distance measure between any two points that would be obtained by measuring the length of the straight line joining the two points is the Euclidean distance. Considering two points in a n-dimensional metric space, the Euclidean distance is given by: ()() ( ) )yx()yx(yxyxyx)Y,X(D t2 nn 2 22 2 11nn ??=?+?+?= LL The Euclidean distance represents a spheroid whose centre is n X and has radius )Y,X(D nn and hence while computing the distance measure equal contribution of each 31 coordinate is assumed. However for statistical pattern recognition, considering the variability of each coordinates is desired while computing the distance between two data objects. The distance computation desired requires applying less weight to high variability components and high weight to low variability components. 3.3.2 Mahalanobis Distance Approach The Mahalanobis Distance classification is similar to the Maximum Likelihood classification except for the class covariances which are all assumed to be equal, hence the method is more efficient [Babiloni 2001]. It is based on correlations between variables by which different patterns can be identified and analyzed. It is a useful way of determining similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set. The Mahalanobis distance from a group of values with mean ( ) n4321 ,,,, ?????=? K and covariance matrix ? for a multivariate vector ( ) n4321 x,x,x,x,xx K= is, ()()?????= ? xx)x(D 1T M Mahalanobis distance can also be defined as dissimilarity measure between two random vectors x r and y r of the same distribution with the covariance matrix ? , ()()yxyx)y,x(d 1T rrrrrr ???= ? The Mahalanobis distance approach has been chosen over other classification approaches as it considers the variance and covariance of the variables as opposed to only the average value. The distance measure is taken as a basis for the calculation of the 32 confidence values for prognostics. The wavelet packet energy signature for a transient strain signal is shown in Figure 10. Mahalanobis Distance Feature 0 5 10 15 20 25 -0.02 0 0.02 0.04 0.06 0.08 0.1 Time (seconds) Ma ha la no bis D i s t a n c e Figure 10 Mahalanobis distance Feature Vector. 33 CHAPTER 4 THE FAST FOURIER TRANSFORM & TIME FREQUENCY ANALYSIS The feature extraction techniques utilized for statistical pattern recognition outlined in this chapter are the Fast Fourier Transform and the Time Frequency Analysis. The mathematics and the various advantages and disadvantages of using these methods have been discussed to help understand the applicability of these methods to perform health monitoring of the transient shock and drop characteristic of electronic assemblies. The Fourier transform decomposes the signal by superimposing weighted sinusoidal functions and measure the similarity of the signal to the sinusoidal basis while a Time Frequency analysis provides the exact behavior of the frequency content and its variation over time. The confidence value for damage monitoring and the hypothesis applied for the computation has been outlined. 4.1 Fourier Tansforms The Fourier transform is widely used signal-analysis tools in real-time signal analysis and has found application in several areas such as speech recognition [New et.al. 2003, Polur et.al. 2005, Prasanthi et.al. 2005, Sakurai et.al. 1984, Tin et.al. 2005, Wang et.al. 1996], biomedical signals [ Cesarelli et.al. 1990, Clayton et.al. 1993, Cote et. al. 1988, Pannizzo et.al. 1988] , image processing [Ahlvers et.al. 2003, Feihong 1990, Uzun et.al. 2003] , in solving differential and integral mathematical equations [Branick 2004, Helms 1967, 34 Olejniczak et.al. 1990, Qing et.al. 2000], geology [Axelsson 1997, Liu et.al. 1988], and astronomy [Kulkarni 1995] . The basic objective of a Fourier transform is to decompose the signal by superimposing weighted sinusoidal functions and measure similarity of the signal to the sinusoidal basis. This causes the frequency attributes of the signal transformed to be exactly described. The Fast Fourier transform is an efficient algorithm to compute the Discrete Fourier transform (DFT), and reduces the computation time for an N size signal from ) 2 ?(N to (N)) 2 ?(Nlog [Chu 2000].The FFT algorithms are based on two classes of decimations techniques, the decimation in time and the decimation in frequency. 4.1.1 Discrete Fourier Transform A discrete Fourier transform is given by the matrix-vector product 1N0,1,n 1N 0k kn N ? k f) n F(? ?=? ? = = LL and the inverse Fourier transform is defined as 1N0,1,k 1N 0n kn N )? n F(? N 1 k f ?=? ? = ? = LL The transient strain signal being processed is represented by k f, and kn N ? is called the twiddle factor where N ? is the Nth root of unity and defined as )N/2sin(i)N/2cos( i/N2- e N ? ???= ? = where 1i ?= . 35 The Discrete Fourier Transform requires the computation of the product of a matrix, called the DFT matrix where the matrix components are the twiddle factors with the signal being transformed represented as a vector. Hence the DFT can be written as ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ? ? ? = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1N f 3 f 2 f 1 f 1)1)(N(N N ? 1)2(N N ? 1N N ?1 1)2(N N ? 4 N ? 2 N ?1 1N N ? 2 N ? 1 N ?1 1111 1N F 2 F 1 F 0 F M L MMMMM L L L M Though the DFT can be applied to any kind of real or complex signal, the computation time required when processing large size signals is a limitation. The various algorithms available to perform the FFT are the Cooley-Tukey FFT algorithm, Prime-factor FFT algorithm, Bruun's FFT algorithm, Rader's FFT algorithm, and Bluestein's FFT algorithm to name a few. 4.1.2 The Radix-2 FFT Algorithm The Cooley-Tukey algorithm arranges the input data in bit-reversed sorted order and builds the output transform using decimation in time technique. The Decimation in time FFT is computed by splitting the data over the odd and even index k of the input signal. 1N,1,0n )k2(n N 12N ok 1k2 f n N )k2(n N 12N ok k2 f) n (F ?=?? ? = + ?+?? ? = =? LL 36 The decimation in frequency FFT is obtained by decimating the frequency spectrum data into even-indexed and odd-indexed sets. The decimation in frequency algorithm is applied when the data is in the frequency domain. As the data obtained by the drop and shock testing of electronic assemblies is in the time domain hence the decimation in time FFT is applied. The basic methodology and the structure for the DIT FFT is shown in Figure 11. After splitting the data to the nth level where N=2 n , the frequency spectra of the 1 sample time domain signal is calculated. As the frequency spectra of a one sample signal is represented by the signal itself, hence the N signals obtained after decimation now represent the frequency spectrum of the signal. To compute the FFT the N frequency spectra obtained is combined in the exact reverse order in which the decimation in time occurred. The individual spectra obtained are combined using the FFT butterfly as shown in Figure 12. 37 Signal Sample Size= N=2 n First Level Decimation Even indexed terms(E 1 ) Sample Size =N/2 First Level Decimation Odd indexed terms(O 1 ) Sample Size =N/2 Second Level Decimation Even indexed terms(EE 2 ) Sample Size= N/4 Second Level Decimation Odd indexed terms(OE 2 ) Sample Size =N/4 Second Level Decimation Even indexed terms(EO 2 ) Sample Size= N/4 Second Level Decimation Odd indexed terms(OO 2 ) Sample Size =N/4 n Level Decimation Even indexed term (E?E n ) Sample Size= 1 n Level Decimation Odd indexed term (O?.E n ) Sample Size= 1 n Level Decimation Even indexed term (E?O n ) Sample Size= 1 n Level Decimation Odd indexed term (O?.O n ) Sample Size= 1 Figure 11 Fast Fourier Transform decimation based on Decimation in time algorithm. 38 The mathematical formulation of the combination of these individual spectra can be understood by looking at the basic FFT transform where 1N,1,0n )k2(n N 12N ok 1k2 f n N )k2(n N 12N ok k2 f) n (F ?=?? ? = + ?+?? ? = =? LL Using the mathematical properties of the twiddle factors the equation can be represented as 1N,1,0n nk 2/N 12N ok 1k2 f n N nk 2/N 12N ok k2 f) n (F ?=?? ? = + ?+?? ? = =? LL as 2 N 2 N ?=? . The computational time for the FFT is further reduced as the DFT sums needed are computed for only half the values of n, i.e. 12/N,1,0n ?= LL , as k) 2 N n( 2 N nk 2 N + ?=? and n N ) 2 N n( N ??= + ? . Representing the two individual DFTs as 12N,1,0nfO 12N,1,0nfE nk 2/N 12N ok 1k2n nk 2/N 12N ok k2n ?=?= ?=?= ? ? ? = + ? = LL LL 39 The FFT of the signal for 12/N,1,0n ?= LL , is given by 12N,1,0nOE)(F n n Nnn ?=?+=? LL The FFT for the signal terms from 1,......2,12 ??= NNNn is given by as 12N,1,0nff)(F k) 2 N n( 2/N 12N ok 1k2 ) 2 N n( N k) 2 N n( 2/N 12N ok k2 2 N n ?=??+?=? + ? = + ++ ? = + ?? LL Using the twiddle factor properties, k) 2 N n( 2 N nk 2 N + ?=? and n N ) 2 N n( N ??=? + the transform is given by 12N,1,0nff)(F nk 2/N 12N ok 1k2 n N nk 2/N 12N ok k2 2 N n ?=????=? ?? ? = + ? = + LL Substituting the values of the two DFT , the transform is given as 12N,1,0nOE)(F n n Nn 2 N n ?=??=? + LL The basic methodology of the combination of the decimated signal is shown in Figure 13 for a two point frequency spectrum combined to produce a four point frequency spectrum [Chu 2000]. 40 O n + - n n N n 2 N n OE)(F ??=? + n n N nn OE)(F ?+=? n N ?? E n ++ - Figure 12 A basic FFT butterfly structure used to combine the decimated signal to obtain the frequency spectrum. Even indexed Two point Frequency Spectrum Odd indexed Two point Frequency Spectrum Four point Frequency Spectrum - + n N ?? + n N ?? -- - Figure 13 Example of the Structural Combination of the decimated signal to produce the frequency spectrum. 41 4.1.3 FFT Frequency Bands Fourier transforms have been shown to be useful while studying the health of machinery and other vibration data. The fast fourier transform is applied to the signal and the signal data is transformed to the frequency domain. The energy of a signal in a given frequency range (f 1 ,f 2 ) is defined by using frequency bands n 2 )n21 d(F),(E 2 1 ??=?? ? ? ? where )(F n ? is the FFT of the time domain signal [ Marple 1987]. The use of FFT Frequency Bands based on Fourier signal analysis is a common way to express and visualize the frequency content of a signal. By utilizing various different frequency ranges the frequency band energies can be calculated and the vector of the energies obtained is used as a feature vector to perform prognostics and damage monitoring. The FFT frequency band energies obtained from the Fourier transform of the various modeshapes and frequencies of vibration of the electronic assembly are the basis for the computation of confidence values for health monitoring. The FFT frequency band energies are the feature vector used to study the transient characteristics of the drop and shock damage occurring in electronic assemblies. The above approach has also been applied to the transient-strain response, the transient -displacement response, vibration modeshapes and frequencies of the electronic assembly under drop and shock. The strain signal and its energy signature are shown in Figure 14 and Figure 15 respectively. The transient strain signal obtained from the strain sensors placed on the electronic assembly while performing drop and shock testing, as explained in section 6.1 has been used to obtain the FFT frequency band signature. 42 Figure 14 : Transient Strain-History at Location of CSP during Drop-Event. 43 Loc 1 Loc 3 Loc 5 Loc 8 Power Spectrum Density 0 20 40 60 80 100 120 140 160 0 200 400 600 800 1000 Frequency (Hz) PS D (d B/H z ) Power Spectrum Density 0 10 20 30 40 50 60 70 80 0 200 400 600 800 1000 Frequency (Hz) PSD (dB/H z ) Power Spectrum Density 0 50 100 150 200 250 300 350 0 200 400 600 800 1000 Frequency (Hz) PSD (d B/H z ) Power Spectrum Density 0 20 40 60 80 100 120 140 160 180 0 200 400 600 800 1000 Frequency (Hz) P S D (d B/Hz) (a) CSP Location 1 (b) CSP Location 3 (d) CSP Location 8(c) CSP Location 5 Fre (Hz) PS D (d B/H z ) P S D (d B/Hz) P S D (dB/Hz ) P S D (d B/Hz) Figure 15 : FFT Frequency Band Energy Feature Vector. 44 0 5 10 15 20 25 0 200 400 600 800 1000 Frequency (Hz) Re ce ptan ce Figure 16 The Receptance Plot obtained by the Modal Analysis of the TABGA Board. (a) Mode 1 (b) Mode 2 (c) Mode 3 Figure 17 The Mode shapes and natural frequencies of vibration of the Board. 45 4.2 Time Frequency Analysis Many signals in real world situations have frequency content that varies over time in the signal. Using a joint time frequency analysis on such a signal provides the exact behavior of the frequency content and its variation over time. This provides an opportunity to study the energy density of a signal simultaneously in time and frequency and also helps in removing noise and interference from the signal. The joint time frequency is classified into two categories, the Linear time frequency transforms and the Bilinear time frequency transforms. Some of the techniques under each class have been outlined in the chart shown in Figure 18. Time Frequency Transform Linear Transforms Bilinear Transforms Short Time Fourier Transform Spectrogram Continuous Wavelet Transform Wigner-Ville Distribution Cohen Class of Distribution Figure 18 Time Frequency Analysis Techniques. 4.3 Linear Time Frequency Transforms The Fourier transform of a signal is performed to obtain the frequency content of the signal and assumes that the signal is stable during the sample time. Hence the frequency spectrum is not able represent the frequency content changing with time and hence the frequency spectrum will not be able to uniquely represent the signal. The main 46 linear transforms used are the Short Time Fourier Transform (STFT) and the continuous wavelet transform (CWT). 4.3.1 Short Time Fourier Transform The STFT is a generalization of the Fourier transform. In the STFT the signal is multiplied with a window function which emphasizes the signal at the specific time and suppresses the signal at other times. The windowed signal is computed as )t(h)(f(f t ???=)? where )?(f t is the modified signal, )(f ? is the original signal and )t(h ?? is the window function centered at t. Hence the signal is suppressed at everywhere and remains unaltered around time t. The Fourier transform of the windowed signal at each time interval is computed and for each time interval a different frequency spectrum is obtained. The totality of these individual spectra is termed as the time-frequency spectrum of the signal. ?)? ? =? ? ??? d(se 2 1 )(S t i t The spectrogram obtained from the energy density spectrum is computed as 2 tSTFT )(S),t(P ?=? The Spectrogram mixes the energy of the signal with the energy of the window and hence does not give results solely for the signal but the resulting transform is equivalent to the spectrum of the signal convolved with the spectrum of the window [Cohen 1995]. Also the window function cannot have high resolution in time and frequency both, hence cannot give good localization in both time and frequency. This 47 limitation is overcome by applying Quadratic time frequency transforms which allows good localization in both the domains. 4.3.2 Continuous Wavelet Transform The continuous Wavelet Transform (CWT) allows a variable coverage of the time-frequency plane [Grossmann et.al. 1984]. The transform is defined as: ? ? ? ? ? ? ? ? ?? ?= ? dt t *)t(f a 1 CWT a, where )t(? is the mother wavelet and ? nd a are the time translation and the scaling parameter respectively. The basic algorithm is based on convolving the signal with a set of functions which are the scaled and dilated version of the mother wavelet. The squared magnitude of the CWT coefficients is equivalent to the power spectrum so that a typical display of the CWT is a representation of the power spectrum as a function of time offset ? and is termed as a scalogram. The exact form of the CWT scalogram depends on the choice of mother wavelet and therefore the extent of the relation between the squared magnitude of the CWT and the actual signal power spectrum is dependent on the signal structure. Since a wavelet does not precisely report information on the frequency of a signal it may not be the best tool for analyzing transient signals containing signal structures similar to sine waveforms. The lack of an orthonormal basis for a CWT means that the CWT does not possess the mathematically simple method of inversion offered by orthonormal DWT?s. 48 4.3.3 Gabor Expansion The Gabor representation is based on expanding a signal in terms of two dimensional time-frequency functions where the coefficients give an indication of the relative weight of the particular expansion function [Gabor1946]. The discretization in the time-frequency plane is computed where the coordinates are ?=? = m nTt i i ?<<?? m,n where i and ? is the time and frequency intervals. The proposed representation of the transient signal is given as tjn m,n m,n m,nm,n e)mTt(h)t(h )t(hc)t(f ? ?= = ? ???= ,m,n where )t(h is a one dimensional functional, mostly the Gaussian as it is most compact in the sense of the time-frequency bandwidth. The square of the coefficients provide the energy at the point ii ,t ? in the time-frequency spectrum. The expansion coefficients obtained from the Gabor expansion are not always unique to a signal and hence not a good choice for performing the time frequency analysis for health monitoring purposes. The biggest shortcoming of the linear time-frequency transforms is that the time- frequency resolution is limited to the Heisenberg bound. This occurs due to imposition of the local time window, which limits the resolution of the frequency window. 49 4.4 Quadratic Time Frequency Transforms The quadratic time frequency transforms are computed by the multiplicative comparison of the signal with itself which is expanded in various directions about each point in time. The representation is in the form of a two dimensional distribution of energy over the time-frequency spectrum. As the quadratic transforms do not use windowing functions hence the resolution problems faced in linear transforms are eliminated. 4.4.1 Wigner-Ville Distribution The Wigner distribution [Wigner 1932, Ville 1948] for a given signal )t(f and its spectrum )(F ? is given as ? ? ++ ? = ? ?? +? ? = d? it? ?)e 2 1 ?)F(? 2 1 (?F 2? 1 d? i? )e 2 ? )f(t 2 ? (tf 2? 1 ?)W(t, The Quadratic nature of the Wigner distribution is attributed to the signal entering twice in the computation. To obtain the distribution at a given time t, the signal at a past time is multiplied with the signal at a future time with the interval considered same, i.e. ? both in the past and in the future. The similarity of the signals to the left of the time t and to the right of the time t are checked and if these parts overlap then the same properties are assumed to be present at time t. The Wigner distribution is identical in both the time and frequency domains. As this distribution weighs the faraway times equally to the near times hence it is highly nonlocal. 50 The characteristic function of the Wigner Distribution, the symmetric ambiguity function is defined as dte) 2 ? )f(t 2 ? (tf ddt'd i?? )e 2 ?' )f(t 2 ?' (tfe 2 1 (Addt,t(We(M ti iti iti ? ??+? ??+? ? ??? ?? +? ? = ?? ?? +? ? ? = )?,?=?)?=)?,? The characteristic function is applied to study and construct the energy densities. It is defined as the Fourier transform of the density titi edt)t(fe(M ?? ==)? ? Hence the characteristic function is the average of the term ti e ? , where ?is a parameter. The 2 dimensional characteristic function used in the Wigner and other Quadratic transforms is just the average of the term containing both frequency and time parameters, i.e. ??+? iti e ?? ?)?=)?,? ??+? ddt,t(We(M iti And hence the density can be computed from the characteristic function by taking the inverse Fourier transform. ?? ??)?,? ? =? ????? dde(M 4 1 ),t(W iti 2 The basic properties of the Wigner distribution are: 1. Real Valued distribution: The Wigner distribution is always real, even when the signal is complex. The mathematical proof of the given property can be obtained by 51 computing the conjugate of the Wigner distribution. )?= ? ? ? ?? += ? ?? ? ?? +?= ? ? +?=? ,t(W - d? i? )e 2 ? -(t * )f 2 ? f(t 2? 1 d? i? )e 2 ? -(t * )f 2 ? f(t 2? 1 d? i? )e 2 ? (t * )f 2 ? f(t 2? 1 ),t(W * 2 Symmetry: The Wigner distribution is symmetric in the frequency domain when the signal has symmetrical spectra and symmetrical in the time domain when the signal is symmetrical. 3 The Wigner distribution satisfies the time-frequency marginals. The energy content in the analyzed signal and the energy content in the Wigner distribution is equal and hence a Wigner-Ville distribution is able to represent the signal?s energy distribution in the time-frequency domain. ??? ? ?? ? ?? ? ?? =?)? 2 )t(fddt,t(W The Wigner distribution for a sum of two signals, termed as the cross Wigner distribution is given by ),t(W),t(W),t(W),t(W),t(W )t(f)t(f)t(f 21122211 21 ?+?+?+?=? += Here the cross Wigner distribution is given by ? ? 2 ? ? 2 ? ? ? =? ??? de)t(f)t(f 2 1 ),t(W i 2 * 112 52 As the Wigner distribution is complex in nature hence * 2112 WW = Due to this property of the cross Wigner distribution the sum of the two cross terms give a real value, i.e. )}?+)?+)?=)?? )}?=+ ,t(WRe{2,t(W,t(W,t(W ,t(WRe{2WW 122211 122112 The signal can be broken into any number of parts and this causes the Wigner distribution to contain the cross reference terms as compared to just the sum of the individual signal Wigner distributions [Mark 1970]. This causes the correlation term to be present in the distribution and this term is not unique and can cause errors while studying the signal distributions. The quadratic nature of the transforms creates cross- terms whenever multiple frequencies are superimposed. This is overcome by using the Cohen Class of Distributions. 4.4.2 Cohen Class of Transforms The Cohen class of transforms apply the approach of computing the time frequency analysis using a kernel, which is a auxiliary function. The kernel can be defined so as to represent the properties and requirements of the particular distribution. The time-frequency representation [Cohen 1989] is given by ??? ??)?,?(? 2 ? + 2 ? ? ? =? ?+????? 2 dddue)u(f)u(f 4 1 ),t(C uiiti* where )?,?(? is the kernel function. 53 In terms of the characteristic function the distribution is represented as )?,?(?)?,?(?= 2 ? + 2 ? ?)?,?(?=?,? ??)?,? ? =? ? ?? ? ????? 2 due)u(f)u(f)(M bygiven isfunction ticcharcteris theand dde(M 4 1 ),t(C ui* iti where )?,?(A is the symmetrical ambiguity function. A list of the various time frequency distributions, their kernels are shown in Table 4-1 shown below for the various distributions studied in this thesis. By the application of the kernel method the time frequency distribution can be obtained such that they satisfy the constraints for any particular distribution. The kernels are defined to satisfy certain conditions and this cause the distributions obtained to also satisfy these conditions and as the generation of the kernels is not very complicated this reduces the computation and mathematical complexity involved. 4.4.3 Reduced Interference Distributions As outlined while studying the Wigner-Ville distribution, the distribution obtained for multicomponent signals contain correlation cross terms and hence causes the distributions to contain data not unique to the particular signal. The time frequency analysis methods proposed by researchers such as Choi and Williams [Choi et.al. 1989] minimize the presence of these cross terms. The cross terms can be reduced by developing kernels that reduce the interference or the cross reference terms, Cohen has proposed and derived one such kernel defined as the Reduced Interference Distribution (RID) kernels. 54 Table 4-1 Kernels applied during Time Frequency analysis [Cohen 1995]. Name Kernel: )?,?(? Distribution: )?,t(C Cohen General Class )?,?(? ??? ?)?,?(? 2 ? + 2 ? ? ? ?+????? 2 ddue)u(f)u(f 4 1 uiiti* Wigner-Ville 1 ? 2 ? + 2 ? ? ? ??? ? de)u(f)u(f 2 1 it* Choi-Williams Distribution ???? 22 / e ?? 2 ? + 2 ? ? ?? ? ?????(?? 2 3/2 2 )u(f)u(fe 1 4 1 *i/)tu 2 STFT Spectogram du) 2 u(h e)u(h ui* ? + 2 ? ? ? ?? 2 i d)t(h(fe 2 1 ? ???)? ? ??? The derivation of the conditions required to form RID kernels has been outlined in [Cohen 1995] and the basic mathematics of the kernel is outlined. 55 ? ?? ? ????? 2 2 ? + 2 ? ?)?,?(?=?,? ??)?,? ? =? due)u(f)u(f)(M bygiven isfunction ticcharcteris theand dde(M 4 1 ),t(C ui* iti Assuming the function to be a sum of two signals as defined while studying the cross Wigner distribution terms ti 1 ti 121 eSeS)t(f)t(f)t(f 21 ?? +=+= Hence the distribution can be defined as 21222 CCCC(C +++=)?,? 111 Computing the characteristic function and the distribution the signal terms and the cross reference terms are given by )( 2 1 where de), 2 1 (R where (ReSSC(ReSSC de 2 S Cde 2 S C )i t)(i 1 * 221 t)(i 2 * 112 )i 2 2 22 )i 2 1 11 12 122 2 2112 ???(?? 21 ???????? ???(?????(?? ?+?=? ????(? ? =)? )?=)?= ?)?(0,? ? =?)?(0,? ? = ? ?? 1 1 Hence to minimize the cross terms the value of the term )?(R has to be minimized. The representation of the )?(R terms shows that for the cross terms to be minimized the value of )?,?(? away from the ? ?and axis should be small as compared to values of ? ?and respectively. The condition outlined is the property of a cross shaped low pass filter and 56 0>>??1<<)?,?(? for In this study the binomial time-frequency kernel proposed by [Jeong et.al. 1992 .] has been applied to study the drop and shock characteristics of an electronic assembly. The kernel proposed satisfies various kinds of conditions such as: 1 The time frequency distribution obtained is not negative and real in value. 2 The RID kernel does not depend on time and frequency and some of the other properties satisfied the kernel are ?=)?(? ?=),?(? )?,????=)?,?(? ? of valuesall for1,0 of valuesall for10 ( 3 The time and frequency marginals are satisfied and the energy of the signal is exactly represented by the distribution. The binomial time-frequency Distribution defined by [Jeong et.al. 1992] is defined as ???? ?+=? ??=? ? ? ?=? ??=? ?? )???+)?+?+ ? ? ? ? ? ? ? ? ?+? ? ? )?=? 4i 2 en(fn(f 2 2 )(g (h),n(TFR where )?)? g( and (h is the frequency smoothing window and the time smoothing window respectively and )n(f represents the signal where N2,1n L= . The term ??=? , and here is used to define the RID kernel as the RID kernel constraint is that 0>>?? . The frequency smoothing window )?(h and the time smoothing window )?g( used here is a hamming window of size(N) as outlined in [Jeong et.al. 1992]. The binomial distribution provides an efficient and fast computation method for computing Time Frequency distributions. The kernel values in the binomial kernel are based on the 57 binomial expansion and a time saving and recursive application is applied by convolving kernel with the autocorrelation using shifts and add-ins [Williams 1996]. 4.5 Time Frequency Moments The Time frequency moments are used in our study to represent the time frequency distribution of the transient signals obtained during the shock and drop testing of electronic assemblies. The time frequency moments are calculated from dt)t(f)W,(C)t(ft nm *mn ?=? ? where )W,T(C nm represents a correspondence term between the time frequency moments and the signal. The value of )W,T(C nm is obtained from the formula 0=?,? ?+? + )?,?? ???? ? = WjTj m mn mn nm e( jj 1 )W,T(C While calculating the moments some of the common correspondence terms )W,T(C nm is shown below in Table 4-2 [Cohen1995] 58 Table 4-2 List of Common Correspondence Functions. Type )W,T(C mn Nonmixed signals 0n when W0m when T mn == Normal WT mn Symmetrization { } nmmn TWWT 2 1 + where lnlm )n,mmin( 0l lmn lmln )n,mmin( 0l lnm TW l m l n !l)i(WT WT l m l n !l)i(TW ?? = ?? = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?= ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?= ? ? In this study the individual time moments and frequency moments of the signal are computed and used as a feature vector to study the damage progression in electronics in a shock and drop event. The above approach has also been applied to the transient-strain response, the transient -displacement response, vibration modeshapes and frequencies of the electronic assembly under drop and shock. The time frequency distribution obtained for a JEDEC standard horizontal drop of an electronic assembly is shown in Figure 19. The moment feature vectors obtained for the transient strain signal are also shown in Figure 20 and Figure 21 respectively. The transient strain signal (Figure 14) obtained from the strain sensors placed on the electronic assembly while performing drop and shock testing, as explained in section 6.2 has been used to obtain the Time Frequency moment signature. 59 Figure 19 Time Frequency Distribution for a Transient Strain signal. 60 Time Moment Feature Vector -2000 -1000 0 1000 2000 3000 4000 5000 0.000 0.002 0.004 0.006 0.008 0.010 Time (sec) Instantaneous Frequency (Hz) Figure 20 Time Moment Feature Vector for a Transient Strain Signal. Frequency Moment Feature Vector -0.02 -0.01 0 0.01 0.02 0.03 0.04 0 200 400 600 800 1000 Frequency (Hz) Instantaneous Time (sec) Figure 21 Frequency Moment Feature Vector for a Transient Strain Signal. 61 4.6 Confidence Value Computation The confidence value of an electronic assembly as described in this study defines the state of reliability of an electronic system in a shock and drop environment. Statistical pattern recognition has been applied to the transient-strain response, the transient- displacement response, vibration modeshapes and frequencies of the electronic assembly under drop and shock. The feature vectors obtained by the various signal processing techniques, Wavelet Energy signature using the wavelet packet transform, Mahalanobis Distance vector using the Mahalanobis distance computation, FFT Frequency Band Energy signature using the Fast Fourier transform and the Time Frequency Moments using Time Frequency analysis are used to determine the health of an electronic assembly. 4.6.1 Testing Hypothesis The statistical hypothesis is defined as an assumption made about a parameter of a given statistical population. The truth of an assumed hypothesis is verified by performing a statistical test on the population. The probability of the occurrence of the event assumed in the hypothesis is calculated and if the probability is above a certain significance level then the hypothesis is considered to be true. The hypothesis assumed in this study is that the means of the two populations being compared are identical, i.e. 0 2 ? 1 ?: a H 0 2 ? 1 ?: 0 H ?? =? The distribution of the assumed hypothesis, i.e. the Null Hypothesis is studied and a statistical test is performed to check whether the Null hypothesis might be rejected in 62 favor of the alternative hypothesis. There are two different types of tests that can be applied while verifying the hypothesis, the one-tailed test and the two-tailed test. The choice between one sided or two-sided test depends on the alternative hypothesis assumed to the null hypothesis. The one sided p ?value is the measure of the evidence against the null hypothesis, 0 2 ? 1 ? =? . If the alternative hypothesis is limited to only one direction of possible inequality i.e. either 21 ?>? or 21 ?<? , and hence only one direction of inequality of means is significant to the problem, then the one sided test is applied. The two sided p value is the measure of the evidence when the alternative hypothesis is unrestricted, i.e. 21 ??? . Hence the two-sided test has been performed on the feature vectors obtained from the statistical pattern recognition techniques. The t- value is obtained using signal theofdeviation standard compared being vectorsfeature signal theof smean value the, where t 21 12 ?? ??? ? ??? = Using the student-t value distribution we obtain the confidence value in our case the reliability of the assembly. A faulty assembly gives a CV of 1 while a CV of a non faulty assembly gives a CV of 1. 63 CHAPTER 5 CLOSED FORM ANALYTICAL MODELS In this study, closed-form models have been developed for the eigen-frequencies and mode-shapes of electronic assemblies with various boundary conditions and component placement configurations. Model predictions have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock-damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response have been quantified. 5.1 Derivation of the Lagrangian Functional For the analytical solution for the free vibration of rectangular plates, the Governing differential equation needs to be developed. The displacement function in the z-direction is assumed to be of the form, ti y)ew(x,t)y,(x, o W ? = where ? is the natural frequency at which the plate vibrates when the mode shape of the plate is given by ),( yxw . 64 The displacement field from the Kirchhoff hypotheses in the pure bending case is given by: (5.1) t)y,(x, o Wt)z,y,w(x, , y o W zt)z,y,v(x, , x o W zt)z,y,u(x, LL ? ? ? ? ? ? ? ? ? ? ? = ? ? ?= ? ? ?= To obtain the strains from the displacement field defined above, the assumption of small strains and displacement has been made. The linear strains obtained from the displacement field are: (5.2) 0 zz ?0, yz ?0, xz ? yx o W 2 z xy ? 2 y o W 2 z yy ? 2 x o W 2 z xx ? LL ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? === ?? ? ?= ? ? ?= ? ? ?= For the derivation of the Governing Differential equation the virtual Lagrangian functional is developed. The Virtual Lagrangian Functional is given by ?K?V?U?L ?+= where Energy Kinetic Virtual?K Energy Potential Virtual?V Energy Strain Virtual?U ? ? ? 65 Using the Hamilton?s Principle (the dynamic version of the principle of virtual displacements): (5.3)0?K)dt?VU(?L T 0 T o LL ? =?+? ? = 5.1.1 Development of the Virtual Strain energy The virtual strain energy of a plate is ( ) () ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ?= ?? ? ++= ? ++= A dzdxdy 2 h 2 h yx 0 ?W 2 z xy 2? 2 y 0 ?W 2 z yy ? 2 x 0 ?W 2 z xx ??U getwe(5.2),equationfromstrainsthengSubstituti dzdxdy A 2 h 2 h xy ?? xy 2? yy ?? yy ? xx ?? xx ? V dV xy ?? xy 2? yy ?? yy ? xx ?? xx ??U The moments per unit length ),,( xyyyxx MMM are given by (5.5) A dxdy yx 0 ?W 2 xy 2M 2 y 0 ?W 2 yy M 2 x 0 ?W 2 xx M?U ismomentstheoftermsinequationenergystraintheHence (5.4) 2 h 2 h dzz xy ? yy ? xx ? xy M yy M xx M LL LL ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? + ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ?= ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? ? 66 Assuming linear elastic behavior of the plate material For isotropic plate where ()?12 E G 66 Q 2 ?1 E 22 Q 2 ?1 ?E 12 Q 2 ?1 E 11 Q + == ? = ? = ? = For an orthotropic material ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? ? xy 2? yy ? xx ? 66 Q00 0 22 Q 12 Q 0 12 Q 11 Q 2 ?1 E xy ? yy ? xx ? where 1 E 2 E 12 ? 21 ? 12 G 66 Q 21 ? 12 ?1 2 E 22 Q 21 ? 12 ?1 2 E 12 ? 12 Q 21 ? 12 ?1 1 E 11 Q == ? = ? = ? = Substituting the above stress-strain relation in equation (5.4) we get, ? ? ? ? ? ? ? ? ? ? + ? ? ?= ? ? ? ? ? ? ? ? ? ? + ? ? ?= 2 x 0 W 2 12 D 2 y 0 W 2 22 D yy M 2 y 0 W 2 12 D 2 x 0 W 2 11 D xx M ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? ? xy 2? yy ? xx ? 66 Q00 0 22 Q 12 Q 0 12 Q 11 Q 2 ?1 E xy 2? yy ? xx ? 2 ?1 00 01? 0?1 2 ?1 E xy ? yy ? xx ? 67 yx 0 W 2 66 2D xy M ?? ? ?= where ij Q 12 3 h ij D = ; h=plate thickness Hence the virtual strain energy is given by (5.6) A dxdy yx 0 ?W 2 yx 0 W 2 66 4D 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 12 D 2 y 0 ?W 2 2 y 0 W 2 22 D 2 x 0 ?W 2 2 x 0 W 2 11 D ?U LL ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? ?? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? + ? ? ? ? = 5.1.2 Development of the Virtual Kinetic Energy The Virtual Kinetic energy is given by dxdy A y 0 W? y 0 W x 0 W? x 0 W 2 I 0 W? 0 W 0 I dzdxdy A 2 h 2 h 0 W? 0 W y 0 W? z y 0 W z x 0 W? z x 0 W z??K equationabovethein(1)indefinedfieldntdisplacemengSubstituti (5.7))dzdxdyw?wv?vu?u A 2 h 2 h ?(?K ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? += ?? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?= ++ ?? ? = &&&& && && &&&& LL&&&&&& where ? is the mass density and I 0 , I 2 are the mass moments of inertia defined as ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? ? 2 h 2 h 12 3 h h ??dz 2 z 1 2 I 0 I 68 Hence the virtual kinetic energy is given by (5.8)dxdy A y 0 W? y 0 W x 0 W? x 0 W 2 I 0 W? 0 W 0 I?K LL &&&& && ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? += 5.1.3 Development of the Virtual Potential Energy Assuming a distributed load q(x, y) applied on the top surface of the plate, a transverse edge force n V ? , an normal edge moment nn M ? applied on a portion of the total boundary of the plate denoted as? and the in-plane compressive and shear forces ( xyyyxx NNN ? , ? , ? ), the virtual potential energy is given by (5.9) ? ds n o ?W nn M ? o ?W n V ? A dxdy o y)?)q(x, A dxdy x o ?W y o W y o ?W x o W xy N ? y o ?W y o W yy N ? x o ?W x o W xx N ? ?V LL ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?+ ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? + ? ? ? ? ?= 5.2 Development of Governing Differential Equation From the virtual Strain energy, the virtual kinetic energy and the virtual potential energy we obtain the virtual Lagrangian functional ?K?V?U?L ?+= 69 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? +? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? + ? ? ? ? ? ?? ? ?? ? + ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? + ? ? ? ? = ? ds n o ?W nn M ? o ?W n V ? A dxdy o y)?)q(x, dxdy A y 0 W? y 0 W x 0 W? x 0 W 2 I 0 W? 0 W 0 I x o ?W y o W y o ?W x o W xy N ? y o ?W y o W yy N ? x o ?W x o W xx N ? yx 0 ?W 2 yx 0 W 2 66 4D 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 12 D 2 y 0 ?W 2 2 y 0 W 2 22 D 2 x 0 ?W 2 2 x 0 W 2 11 D &&&& && Assumptions: 1. Assuming that the plate is undergoing free vibration, hence no external force acts on the plate, i.e. q(x, y), n V ? , nn M ? , xyyyxx NNN ? , ? , ? =0. 2. Neglecting the rotary inertia to be zero, i.e. I 2 =0 Hence () ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? = A dxdy 0 W? 0 W o I 2 y 0 ?W 2 2 y 0 W 2 yx 0 ?W 2 yx 0 W 2 ?)2(1 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 ? 2 x 0 ?W 2 2 x 0 W 2 D?L && 70 The weak form of the Lagrangian Functional for isotropic plates () (5.10) A dxdy 0 W? 0 W o I 2 y 0 ?W 2 2 y 0 W 2 yx 0 ?W 2 yx 0 W 2 ?)2(1 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 ? 2 x 0 ?W 2 2 x 0 W 2 D LL && ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? The weak form of the Lagrangian Functional for orthotropic plates ()(5.11)dxdy A o W? o W o I 2 y o ?W 2 2 y o W 2 22 D yx o ?W 2 yx o W 2 66 4D 2 y o ?W 2 2 x o W 2 2 x o ?W 2 2 y o W 2 12 D 2 x o ?W 2 2 x o W 2 11 D LL && ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? To compute the Governing Differential Equation for a Plate from the weak form, we integrate by parts to relieve 0 W? of any differentiation. 5.2.1 Isotropic plates Considering the Strain energy part, dxdydt T 0 b 0 a 0 2 y 0 ?W 2 2 y 0 W 2 yx 0 ?W 2 yx 0 W 2 ?)2(1 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 ? 2 x 0 ?W 2 2 x 0 W 2 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? 71 Integrating by parts + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? T 0 b 0 a 0 dxdydt x 0 ?W 3 x 0 W 3 D T o bt x 0 ?W 2 x 0 W 2 D ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ? ? ?? ? ? ? ? ? ?? ? ? + ??? ? ? ?? ? ? ? ? ? ? + ??? ? ? ?? ? ? ? ? ? ? dxdydt T 0 b 0 a 0 x 0 ?W x 2 y 0 W 3 T 0 a 0 dx x 0 ?W yx 0 W 2 v)2D(1 T 0 b 0 a 0 dxdydt y 0 ?W y 2 x 0 W 3 T 0 t y 0 ?W 2 x 0 W 2 T 0 b 0 a 0 dxdydt x 0 ?W x 2 y 0 W 3 T 0 t x 0 ?W 2 y 0 W 2 D? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ? ? ? ? ? ? ? ? ? + T 0 t b 0 a 0 dxdyd y 0 ?W 3 y 0 W 3 D T 0 at y 0 ?W 2 y 0 W 2 D Assuming that all terms evaluated at t=0 and t=T are zero, and again integrating by parts. ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ?? ? ? ? ? ? ? ? ? ? ? ? ?? ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??? ?? ? ? ? ? ? ? ? ? ? ? ?? ? + ? ? ? ? ? ? ? ? ??? ?? ? ? ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ? ? ??? ? ? + ? ? ? ? ? ? ? ? ? ? ?? T 0 t b 0 a 0 dxdyd 0 ?W 4 y 0 W 4 D T 0 at 0 ?W 3 y 0 W 3 D dxdydt T 0 b 0 a 0 0 ?W 2 x 2 y 0 W 4 T 0 b 0 dy 0 ?W 2 yx 0 W 3 v)2D(1 T 0 b 0 a 0 dxdydt 0 ?W 2 y 2 x 0 W 4 T 0 t 0 ?W y 2 x 0 W 3 T 0 b 0 a 0 dxdydt 0 ?W 2 x 2 y 0 W 4 T 0 t 0 ?W x 2 y 0 W 3 Dv T 0 b 0 a 0 dtdydx 0 ?W 4 x 0 W 4 D T 0 bt 0 ?W 3 x 0 W 3 D 72 All terms evaluated at t=0 and t=T are assumed to be zero, dxdydt T 0 b 0 a 0 0 ?W 4 y 0 W 4 D 2 x 2 y 0 W 4 v)2(1 2 x 2 y 0 W 4 2v 4 x 0 W 4 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?+ ?? ? + ? ? Hence the Strain energy term after integration by parts is, ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? + ? ? T 0 b 0 dtdydx a 0 o ?W 4 y o W 4 D 2 y 2 x o W 4 2 4 x o W 4 D Integrating by Kinetic energy term by parts, we get [] dxdy T 0 b 0 a 0 o ?W o W o I T o ab o ?W o W o Idxdydt T 0 b 0 a 0 o W? o W o I ??? ?= ??? &&&&& Hence the Kinetic Energy term after integration by parts is, dxdy T 0 b 0 a 0 o ?W o W o I ??? ?= && Hence the Hamilton?s Equation is denoted as, () dxdydt T 0 b 0 a 0 0 ?W 0 W o I 4 y 0 W 4 D 2 x 2 y 0 W 4 2 4 x 0 W 4 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? + ?? ? + ? ? && Applying the Fundamental of Lemma, we get the Euler-Lagrange equation or the Governing Differential equation as () (5.12)0 0 W o I 4 y 0 W 4 D 2 x 2 y 0 W 4 2 4 x 0 W 4 D LL && =+ ? ? + ?? ? + ? ? 73 5.2.2 Orthotropic plates Considering the Strain energy part, dxdydt T 0 b 0 a 0 2 y 0 ?W 2 2 y 0 W 2 22 D yx 0 ?W 2 yx 0 W 2 66 4D 2 y 0 ?W 2 2 x 0 W 2 2 x 0 ?W 2 2 y 0 W 2 12 D 2 x 0 ?W 2 2 x 0 W 2 11 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? Integrating by parts as performed in the section for Isotropic plates the Strain energy term is computed, dxdydt T 0 b 0 a 0 o ?W 4 y o W 4 22 D 2 x 2 y o W 4 ) 12 D 66 2(2D 4 x o W 4 11 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ++ ? ? Integrating the Kinetic energy term by parts, we get [] dxdy T 0 b 0 a 0 o ?W o W o I T o ab o ?W o W o Idxdyd T 0 b 0 a 0 o W? o W o I ??? ?= ??? &&&&& Hence the Kinetic Energy term after integration by parts is, dxdy T 0 b 0 a 0 o ?W o W o I ??? ?= && Hence the Hamilton?s Equation is denoted as, () dxdydt T 0 b 0 a 0 o ?W o W o I 4 y o W 4 22 D 2 x 2 y o W 4 ) 12 D 66 2(2D 4 x o W 4 11 D ??? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? + ?? ? ++ ? ? && Applying the Fundamental of Lemma, we get the Euler-Lagrange equation or the Governing Differential equation as () (5.13)0 o W o I 4 y 0 W 4 D 2 x 2 y 0 W 4 ) 12 D 66 2(2D 4 x 0 W 4 11 D LL && =+ ? ? + ?? ? ++ ? ? 74 To solve the Governing Differential equation derived above, the Ritz method of solving the free vibration problem of rectangular plates is applied [Leissa 1969, Young 1950]. The Rectangular plate is considered as a mesh of plate strips, with the plate functionals satisfying the specific boundary conditions of the plate. 5.3 Plate Functional Derivation using Plate Strips The method assumes that the plate strip being analyzed is very thin, i.e. a plate strip in the x-direction has the y-axis thickness so negligible that the y-component of the displacement field to be neglected. 5.3.1 Plate Strip Displacement Function To assume the functional W(x, y), we analyze the free vibrations of plate strips having the boundary conditions we desire. The Governing Differential Equation for the plate derived for the free vibration of rectangular plates is ()0 0 W o I 4 y 0 W 4 D 2 x 2 y 0 W 4 2 4 x 0 W 4 D =+ ? ? + ?? ? + ? ? && Considering a very thin plate strip in the x-direction such that the y-component of the displacement function can be neglected, i.e. tcos?W(x)t)(x, o W = Hence the GDE can be written as 0 2 t 0 W 2 o I 4 x 0 W 4 D = ? ? ? ? ? ? ? ? ? ? ? ? + ? ? 75 Substituting the assumed solution in the governing differential equation: 0W 2 ? 0 I 4 dx W 4 d D =? Denoting the above equation as 0cW 2 dx W 2 d b 4 dx W 4 d a =?+ where Da = ; 0b = ; 0 I 2 ?c = Assuming rx AeW(x) = 0c 2 br 4 ar =?+? Putting s 2 r = 0cbs 2 as =?+ The roots of the equation are 2 l 2 ? )4ac 2 bb( 2a 1 2 s 2 l 2 ? )4ac 2 bb( 2a 1 1 s =++?= =+??= The solution can be expressed as )14.5( l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 cW(x) LL+++= where )4ac 2 bb( 2a 1 l ? +??= ; )4ac 2 bb( 2a 1 l ? ++?= As b=0, hence ?=? ; 76 l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 cW(x) +++=? Based on the specific boundary conditions of the plate the functionals are developed further. 5.3.2 For Simple-Simple plate strip The boundary conditions for a free-free plate strip are given below. Displacement at simply supported edges is zero. 0W = ; at x=0,l Moments at the simply supported edges are zero: 0 2 x W 2 D xx M = ? ? ?= ; at x=0,l From section 5.3.1 using the displacement field derived, given by equation (5.14), the displacement and the second level derivatives of the displacement field are calculated. l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 cW(x) +++= ) l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 c( 2 2 l ? 2 x W 2 ++??= ? ? Applying the boundary conditions: At x=0 2 c 4 c0 xx M 4 c 2 c0W =?= ?=?= The above case is only possible if 0 4 c 2 c == At x=l and substituting 0 4 c 2 c == 77 (5.16)0)sinh 3 csin? 1 c(0 xx M (5.15)0)sinh 3 csin? 1 (c0W LL LL =+??= =+?= ? ? 0 3 c 1 c sinh?sin? sinh?sin? = ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ` For c 1 , c 3 to have distinct non-zero values the determinant should be zero 02sinh?sin =? ? Hence the transcendental equation becomes 0sin?sinh =? from which we obtain the eigenvalues of the plate strips using numerical techniques. We use Matlab to obtain values of the above equation. To calculate c 1 and c 3 we solve the equations (5.15) and (5.16) sinh? sin? 1 c 3 c ? =? Assuming c 1 =c, and substituting the calculated values of? , we get 0 3 c = for all values of ? . Hence strip functional is given by l ?x sinW(x) = 78 Table 5-1 Roots of the transcendental equation for a simply supported-simply supported plate. r r ? 1 0 2 3.1416 3 6.2832 4 9.4248 5 15.7080 6 18.8496 7 21.9911 8 21.9911 9 25.1327 5.3.3 Free-Free plate strip The boundary conditions for a free-free plate strip are given below. Moments at the free edges are zero: 0 2 x W 2 D xx M = ? ? ?= ; at x=0,a Shear Force at free edges is zero: 0 3 x W 3 D x V = ? ? ?= ; at x=0,a From the previous section using the displacement field derived, given by equation (5.14), 79 the second and third level derivatives of the displacement field are calculated. ) l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 c( 2 2 l ? 2 x W 2 ++??= ? ? ) l ?x sinh 4 c l ?x cosh 3 c l ?x sin 2 c l ?x cos 1 c( 3 l 3 ? 3 x W 3 +++?= ? ? Applying the boundary conditions: At x=0 At x=a and substituting equations (5.17) and (5.18) 0sinh?i 2 ccosh? 1 csin? 2 ccos? 1 c(0 x V 0cosh?o 2 csinh? 1 ccos? 2 csin? 1 c(0 xx M =+++??= =++???= 0 2 c 1 c sinh?sin?cos?cosh? cos?cosh?sinh?sin? = ? ? ? ? ? ? ? ? ? ? ? ? ? ? +? ?+? For c 1 , c 2 to have distinct non-zero values the determinant should be zero 022cosh?cos =?? ? Hence the transcendental equation becomes 01coscosh =??? from which we obtain the eigenvalues of the plate strips using numerical techniques. We use Matlab to obtain values of the above equation. () ()5.18......... 3 c 1 c0 x V 5.17...... 2 c 4 c0 xx M =?= =?= 80 Table 5-2 Roots of the transcendental equation of a Free-Free plate. r r ? 1 0 2 0 3 4.73 4 7.8532 5 10.996 6 14.137 7 17.279 8 20.42 9 23.562 10 26.704 To calculate c 1 and c 2 we solve the equations (5.17) and (5.18) cos?cosh? 1 2 c sinh?sin? 1 1 c ? =? ? =? The value of 2 c 1 c r ? = The functional for a free-free plate strip used in this research is given below. ) l 2x (13 2 X 1 1 X ?= = 81 ? ? ? ? ? ? +?+= l x r ? sin l x r ? sinh r ? l x r ? cos l x r ? cosh r X r=3,4,5,7?. Table 5-3 Values of ? r for a free-free plate. r r ? 3 0.9825 4 1.0008 5 0.99997 6 1 7 1 5.3.4 For a Clamped-Free Strip The boundary conditions for a clamped-free plate strip are given below. For Clamped end: Displacement at clamped edge is zero. 0W(x) = ; at x=0 First Order differential of displacement at clamped edge is zero. 0 dx dW(x) = ; at x=0 For Free end: Moments at the free edges are zero: 0 2 x W 2 D xx M = ? ? ?= ; at x=l 82 Shear Force at free edges is zero: 0 3 x W 3 D x V = ? ? ?= ; at x=l From section 4.3.1 using the displacement field derived, given by equation (5.14), the displacement and the first, second and third level derivatives of the displacement field are calculated. Applying the boundary conditions, )(5.10=)????????? = = ? ? ? ? ? ? ? ? ++??= = ? ? ?= ?=?=+? =++?= = ?=?=+= 9cosh 2 csinh 1 ccos 2 csin 1 c( 0 lx ) l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 c( 2 l l ? *D lx 2 x W 2 D xx M 3 c 1 c0 3 c 1 c 0sinh(0) 4 c l ? cosh(0) 3 c l ? sin(0) 2 c l ? cos(0) 1 c l ? 0x dx dW(x) 4 c 2 c0 4 c 2 cW(0) KK 0 2 c 1 c sinh?sin?cos?cosh? cos?cosh?sinh?sin? ..(5.20)0.........sinh?i 2 ccosh? 1 csin? 2 ccos? 1 c( 0 lx ) l ?x sinh 4 c l ?x cosh 3 c l ?x sin 2 c l ?x cos 1 c( 3 l 3 ? *D lx 3 x W 3 D x V = ? ? ? ? ? ? ? ? ? ? ? ? ??? ???? =??+?? = = ? ? ? ? ? ? ? ? +++?= = ? ? ?= For c 1 , c 2 to have distinct non-zero values the determinant should be zero 022cosh?cos =+?? 83 Hence the transcendental equation becomes 01coscosh =+?? from which we obtain the eigenvalues of the plate strips using numerical techniques. We use Matlab to obtain values of the above equation. Table 5-4 Roots of the transcendental equation of a Clamped-Free plate. r r ? 1 1.8751 2 4.6941 3 7.8548 4 10.996 5 14.137 6 17.279 7 20.42 8 23.562 9 26.704 10 29.845 To calculate c 1 and c 2 we solve the equations (5.19) and (5.20) cos?cosh? 1 2 c sinh?sin? 1 1 c + ? =? + =? The value of 2 1 c c r =? 84 Table 5-5 Values of ? r for a Clamped-Free plate. r r ? 1 0.7341 2 1.0185 3 0.99922 4 1 5 1 6 1 7 1 8 1 9 1 10 1 5.3.5 For Clamped-Clamped plate strip The boundary conditions are given below for a Clamped-Clamped plate strip. Displacement at clamped edge is zero. 0W(x) = ; at x=0,l First Order differential of displacement at clamped edge is zero. 0 dx dW(x) = ; at x=0,l From section 5.3.1 using the displacement field derived, given by equation (5.14), the displacement and the first level derivatives of the displacement field are calculated. l ?x cosh 4 c l ?x sinh 3 c l ?x cos 2 c l ?x sin 1 cW(x) +++= 85 ) l ?x sinh 4 c l ?x cosh 3 c l ?x sin 2 c l ?x cos 1 (c l ? x W ++?= ? ? Applying the boundary conditions : At x=0 At x=l and substituting eqns (5.21) and (5.22) 0sinh?i 2 ccosh? 1 csin? 2 ccos? 1 (c0 dx dW(x) 0cosh?o 2 csinh? 1 ccos? 2 csin? 1 (c0W(x) =????= =??+?= 0 2 c 1 c sinh?sin?cos?cosh? cos?cosh?sinh?sin? = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??+? +?? For c 1 , c 2 to have distinct non-zero values the determinant should be zero Hence the transcendental equation becomes 01coscosh =??? from which we obtain the eigenvalues of the plate strips using numerical techniques. We use Matlab to obtain values of the above equation. To calculate c 1 and c 2 we solve the equations (5.21) and (5.22) cos?cosh? 1 2 c sinh?sin? 1 1 c ? =? ? =? The value of 2 c 1 c r ? = ( ) ()5.220......... 3 c 1 c0 dx dW(x) 5.210...... 4 c 2 c0W(x) =+?= =+?= 86 Table 5-6 Roots of the transcendental equation of a Clamped-Clamped plate. r r ? 1 4.73 2 7.8532 3 10.996 4 14.137 5 17.279 6 20.42 7 23.562 8 26.704 Table 5-7 Values of ? r for a Clamped-Clamped plate. r r ? 1 0.9825 2 1.0008 3 0.99997 4 1 5 1 5.4 Application of Ritz Method The Rectangular plate can be considered as a mesh of plate strips in both the x and y direction and hence the functionals for the Ritz method are same as the functionals for plate strips depending on the boundary conditions at the edges of the plate. The Ritz 87 method has been applied to the various boundary conditions studied in this research. The solution has been outlined for the completely free (FFFF) boundary case. All other boundary conditions are analyzed in a similar manner as shown below. 5.4.1 Completely Free (FFFF) Plate In this case, i.e. the FFFF plate, we have to consider 2 plate strips with Free-Free (FF) boundary conditions, one in the x-direction and the other in y-direction. (y) n (x)Y m X mn Ay)(x, 0 W iwt y)e(x, o Wt)y,W(x, = = where (x) m X is the functional for FF strip in the horizontal direction and (y) n Yis the functional for FF strip in the vertical direction. ) a 2x (13 2 X 1 1 X ?= = ? ? ? ? ? ? +?+= a x m ? sin a x m ? sinh m ? a x m ? cos a x m ? cosh m X m=3,4,5,7?. Similarly ) b 2y (13 2 Y 1 1 Y ?= = ? ? ? ? ? ? +?+= b y n ? sin b y n ? sinh n ? b y n ? cos b y n ? cosh n Y n=3,4,5,7?. Applying the Ritz method using the above assumed solution to the weak form of the GDE i.e. 88 dxdy a 0 b 0 W?W o I 2 y ?W 2 2 y W 2 yx ?W 2 yx W 2 ?)2(1 2 y ?W 2 2 x W 2 2 x ?W 2 2 y W 2 ? 2 x ?W 2 2 x W 2 D ?? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?? ? ?? ? ?+ ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? = && ? = ? = ? ? = ? ? ++ ? ? + ? ? + ? ? = m 1i n 1j ij ?A ij A ? mn ?A mn A ? ......... 13 ?A 13 A ? 12 ?A 12 A ? 11 ?A 11 A ? ?? The Ritz method states that for {A} to be linearly independent 2....n1,j2.....m;1,ifor0 ij A ? === ? ? Assuming ? = ? = = ? = ? = = p 1p q 1q n Y m X mn ?A 0 ?W p 1i k Y q 1j i X ik A 0 W dxdy a 0 b 0 n Y m X k Y i X 0 I 2 ? 2 y n Y 2 m X 2 y k Y 2 i X y n Y x m X y k Y x i X v)2(1 2 y n Y 2 m X k Y 2 x i X 2 n Y 2 x m X 2 2 y k Y 2 i X v n Y 2 x m X 2 k Y 2 x i X 2 D ik A ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? + ? ? ? ? = ? ? 89 Multiplying the whole equation by a 2 to help simplify the mathematics we get () () ()()[] dxdy a 0 b 0 n Y k Y m X i X 2 a 0 I 2 ? 2 y n Y 2 2 y k Y 2 m X i X 2 a y n Y y k Y b x m X x i X a b a v)2(1 2 y n Y 2 k bY m X 2 x i X 2 a n Y 2 y k Y 2 b 2 x m X 2 i aX v b a n Y k Y 2 x m X 2 2 x i X 2 2 a D ik A ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? The integrals required for the above analysis are: miwhen0 miwhen 3 a 4 i ?a 0 dx 2 x m X 2 2 x i X 2 ?= == ? ? ? ? ? This relation is obtained as the FF strip functionals are orthogonal and their second derivatives are also orthogonal. miwhen0 a 0 miwhenadx m X i X ?= ? == nkwhen0 nkwhenbdy b 0 n Y k Y ?= == ? 90 nkwhen0 n.kwhen 3 b 4 n b 0 dy 2 y n Y 2 2 y k Y 2 ?= = ? = ? ? ? ? ? To simplify the mathematical representations the integrals are represented by the following terms [Young 1950], dx a 0 2 dx m X 2 d i Xa im E ? = ? = a 0 dx 2 dx i X 2 d m Xa mi E dy b 0 2 dy k Y 2 d n Yb nk F ? = ? = a 0 dx dx m dX dx i dX a im H ? = b 0 dy dy n dY dy k dY b kn K Hence for i=m, the equation comes out to be 0dy a 0 b 0 k Y k Ydx i X i X 2 a 0 I 2 ? b 0 dy 2 y k Y 2 2 y k Y 2 a 0 dx i X i X 2 Da a 0 b 0 dy y k Y y k Y bdx x i X x i X a b a v)D2(1 a 0 b 0 dy k Y 2 y k Y 2 bdx 2 x i X 2 i aXv b a 2D a 0 b 0 dy k Y k Ydx 2 x i X 2 2 x i X 2 2 Da = ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ?+ ? ? ? ? ? ? ? ? ?? ? ? ? ? + ? ? ? ? ? ? ? ? ?? ? ? ? ? ()( )[] ()( )[] () () 0 2 b a 2 a 0 I 2 ? 4 b k? 2 b a 2 Da kk K ii H b a v)D2(1 kk F ii Ev b a 2D 2 b 3 a 4 i ? 2 Da = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ?++ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? dy b 0 2 dy n Y 2 d k Yb kn F ? = 91 () () 0 2D b 3 a 0 I 2 ? kk K ii H b a v)2(1 kk F ii vE b a 2 3 2b 4 k? 3 a 2a 4 i b? 0 2 b 3 a 0 I 2 ? kk K ii H b a v)2(1 kk F ii vE b a 2 3 2b 4 k? 3 a 2a 4 i b? D =? ? ? ? ? ? ? ? ? ?+++? =? ? ? ? ? ? ? ? ? ?+++? For mi ? the equation comes out to be 0dy a 0 b 0 n Y k Ydx m X i X 2 a 0 I 2 ? b 0 dy 2 y n Y 2 2 y k Y 2 a 0 dx m X i X 2 Da a 0 b 0 dy y n Y y k Y bdx x m X x i X a b a v)D2(1 dy a 0 b 0 2 y n Y 2 k bYdx m X 2 x i X 2 ady b 0 n Y 2 y k Y 2 b a 0 dx 2 x m X 2 i aXv b a D a 0 b 0 dy n Y k Ydx 2 x m X 2 2 x i X 2 2 Da = ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? ?+ ? ? ? ? ? ? ? ? ?? ? ? ? ? + ? ? ? ? ? ? + ? ? ? ? ? ? ? ? ?? ? ? ? ? ()()[] ()()( )( )[]( )( )[ ] ()()[] ()()[] 0 kn K im H b a v)2(1) kn F mi E nk F im v(E b a 0 kn K im H b a v)2(1) kn F mi E nk F im v(E b a D 000 2 a 0 I 2 ?00 2 Da kn K im H b a v)D2(1 kn F mi E nk F im Ev b a D00 2 Da = ? ? ? ? ? ? ?++? = ? ? ? ? ? ? ?++? =?+ ?+++? 92 This can be represented as an Eigenvalue problem, i.e. 0 mn ?? (ik) mn C =? where () mnikwhenKH b a )v1(2)FEFE(v b a mnikwhenKH b a )v1(2FvE b a 2 b2 ka a2 b C knimknminkim kkiikkii 3 434 i)ik( mn ? ? ? ? ? ? ? ?++ = ? ? ? ? ? ? ? ? ?++ ? + ? = D2 baI 3 0 2 ? =? Hence the equation reduces out to where: 2D b 3 a 0 I 2 ? ? = where h 0 ? 0 I = 1 mn ? = for mn=ik = 0 for ikmn ? The Eigenvalues for equation (5.23) are obtained using iterative techniques, in this case by using Matlab. The coefficients A mn can be calculated separately for the symmetrical and asymmetrical deflections by an iterative method devised by Ritz. ).....(5.23.......... p 1m 0 q 1n mn A mn ?? (ik) mn C? = =? = ? ? ? ? ? ? ? 93 The coefficients for symmetrical group are calculated as follows: As 0............)( 0............)( 21 13 2115 13 1513 13 1311 13 11 21 11 2115 11 1513 11 1311 11 11 =++?+ =+++? ACACACAC ACACACAC ? ? These are the two of the nine equations that can be written for the symmetric modes of the SSFF plate. To calculate these coefficients for each mode, we assume one of the constants to be 1, which here are the constants being multiplied to the diagonals of the C matrix. For example we assume 11 A =1 and the rest of the constants as 0. ....... 21 11 2115 11 1513 11 13 11 11 ++++=? ACACACC? Next we calculate each of the other constants using the above ? value: ? ? ?+++?= ?+++?= )/()..........( )/()..........( 11 1521 11 2113 11 13 11 1115 11 1321 11 2115 11 15 11 1113 ? ? CACACCA CACACCA The values obtained from this first iteration are substituted back into the above described process until the successive values obtained for ? and A?s are close to the desired accuracy. The same process is repeated for the antisymmetrical modes. 94 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Length (inches) Mode 2 W idt h ( inc he s ) 0 2 4 6 8 10 0 2 4 6 8 10 -3 -2 -1 0 1 2 3 Width (inches) Mode 2 Length (inches) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Length (inches) Mode 1 W idt h ( inc h e s ) 0 2 4 6 8 10 0 2 4 6 8 10 -3 -2 -1 0 1 2 3 Width (inches) Mode 1 Length (inches) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Length (inches) Mode 4 W idt h ( inc h e s ) 0 2 4 6 8 10 0 2 4 6 8 10-3 -2 -1 0 1 2 3 Width (inches) Mode 4 Length (inches) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Length (inches) Mode 3 W idt h ( inc h e s ) 0 2 4 6 8 10 0 2 4 6 8 10 -3 -2 -1 0 1 2 3 Width (inches) Mode 3 Length (inches) Mode 1 Mode 2 Mode 3 Mode 4 Figure 22 Modeshape Correlation of a Completely Free plate with [Leissa 1969]. 95 5.5 Point Mass Components on the PCA As derived in the sections above, the equation of motion of a uniform rectangular free plate is given by )24.5()t,y,x(p t )t,y,x(w )t,y,x(wD 2 2 4 E KKK= ? ? ?+? where 4 4 22 4 4 4 4 yyx 2 x ? ? + ?? ? + ? ? =? is the biharmonic operator. )]v1(12[ Eh D 2 3 E ? = is the Flexural Rigidity. p (x, y, t) is the transverse external loading. E is the Young?s Modulus, h is the thickness, v is the Poisson ratio, ? is the mass per unit area of the plate, w(x, y, t) is the transverse deflection at position (x, y) and time t. Applying Mode superposition theory, the value of w(x, y, t) due to forced vibration may be obtained from [Wu et.al. 1997a, b] )25.5()t(q)y,x(W)t,y,x(w 'n 1i ii KKK ? = = where )y,x(W i is the i th normal mode shape of the free plate, q i (t) is the i th generalized co-ordinate, and n? is the mode number. The mode shapes are arranged in order of the modal frequency. The normalized mode shapes are given by )y,x(WC)y,x(W iii = 96 where C i is determined by 1dAWW i A i =? ? Substituting equation (5.25) into equation (5.24), and multiplying both sides by )y,x(W j . Integrating the obtained expression over the area A of the plate and applying the orthogonality of the mode shapes we obtain dA A j Wt)y,p(x,(t) j PdA j W A 4 E D j W jj KdA j W A ? j W jj M where (5.26)n'1,j(t), j P(t) j q jj K(t) j q jj M ? = ? ?= ? = ==+ KKK&& which represent the generalized mass, generalized stiffness and generalized force, respectively. If j W is a normal mode shape (with respect to ?), then 1M jj = and equation (5.26) reduces to n'1,j(t). j P(t) j q 2 j ?(t) j q K&& ==+ where jj K jj M jj K j ? == is the j th natural frequency of the free plate. 5.5.1 Eigenvalue Equation of a Constrained Plate If the inertia forces of the concentrated masses are considered the external exciting forces, then the forced vibration equation for a free plate may be used to determine the natural frequencies and mode shapes of the constrained plate. 97 The external forces on the plate during free vibrations are given by ? = ? ? ?= ? ? ?= n' 1i ) a y, a (x i W 2 t (t) i q 2 m 2 t t), a y, a w(x 2 mt), a y, a (xP where m is the point mass present at point (x a , y b ). Hence the equation of motion obtained for a constrained plate is n'1,j(t) i q n' 1i ) a y, a (x i W) a y, a (x j Wm(t) j q 2 j ?(t) j q K&&&& =? = ?=+ For the constrained plate to perform harmonic free vibration, the generalized co-ordinate takes the form t?i e j q(t) j q = where j q is the amplitude of q j (t) and ? is the natural frequency of vibration of the constrained plate. Hence n'1,j i q) a y, a (x i W n' 1i ) a y, a (x j Wm 2 ? j q 2 ? j q 2 j ? K=? = += Let {} { } { } { } [] ?? ?? {}{} )] a y, a (xWm[ n'xn' [A] T WW n'xn' ]W[ n'xn' 11,1, n'xn' [I] n'xn' 2 n' ? 2 2 ?, 2 1 ? n'xn' 2 ? x1n'n' q, 2 q, 1 q x1n' q x1n'n' W, 2 W, 1 W x1n' W == === == KKKK KKKK Hence the equation can be written as {} {}q[A])([I] 2 ?q] 2 [? += 98 Putting [B][A][I] =+ , we get the eigenvalue problem {} {}q[B] 2 ?q] 2 [? = We can solve the above eigenvalue problem for j ? , the j th natural frequency of the constrained plate and the corresponding eigenvector{} )n'1,....,(j (j) q = . Calculating the Eigen vectors: For first eigenvector ? 1 [] 0 30 q . . 2 q 1 q 3030 A' 1 ? 3030 B'... 3030 A' 1 ? 3030 B' ..... ..... ..... 130 A' 1 ? 130 B'.. 12 A' 1 ? 12 B' 11 A' 1 ? 11 B' 0{q}][A' 1 ?][B' ]{q}[A' 1 ?]{q}[B' = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ??? ? =?? = Hence we get 30 equations in 30 unknowns which are be solved to get the eigenvectors corresponding the the eigenvalue ? 1 . The Mode shapes are given by {} (j) }q{ T y)(x,W (j) i q n' 1i y)(x, i Wy)(x, j w ~ =? = = The closed form model for the vibration of electronic assemblies with components represented as point masses is used to study the affect of the location of these electronic components on the dynamic characteristics of the assemblies during drop and shock. Case studies have been performed to study the affect of the fall off of components from the assembly on the transient response of the PCA. An analytical model for the vibration 99 characteristics of a 0.5 mm pitch, 132 I/O, 8 mm flex-substrate CSP has been created. The model structure of the assembly has been shown in Figure 23. Figure 23 Point Mass representation of the Electronic Assembly. 100 CHAPTER 6 APPLICATION AND VALIDATION OF PREDICTIVE MODEL In this study, statistical pattern recognition and leading indicators of shock- damage have been used to study the damage initiation and progression in shock and drop of electronic assemblies. Closed-form models that have been developed for the eigen- frequencies and mode-shapes of electronic assemblies with various boundary conditions and component placement configurations. have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock-damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response have been quantified. A damage index for Experimental Damage Monitoring has been developed using the failure indicators. 6.1 Development of Training Signal and High-Speed Measurement Transient Dynamic Response Three test boards have been used to study the reliability of fine-pitch ball-grid arrays in transient-shock. The packages on one of the test board are 27 mm ball-grid array, 1 mm pitch, 676 I/O; 10 mm Tape Array, 0.8 mm pitch, 144 I/O; 16 mm Flex BGA, 0.8 mm pitch, 280 I/O; 7 mm CABGA, 0.5 mm pitch, 84 I/O; 15 mm ball-grid array, 1 mm pitch, 196 I/O; 6mm Tape Array, 0.5 mm pitch, 64 I/O. (Table 6-1). Each 101 10 mm, 144 I/O Tape Array 27mm, 676 I/O PBGA 16 mm, 280 I/O Flex BGA 7mm, 84 I/O CABGA 15 mm, 196 I/O PBGA 6 mm, 64 I/O Tape Array Figure 24 Interconnect array configuration for Test Vehicles. 102 Table 6-1: Test Vehicles. 10 mm TABGA 27 mm PBGA 7 mm CABGA 16 mm Flex BGA 15 mm PBGA 6 mm TABGA I/O 144 676 84 280 196 64 Pitch (mm) 0.8 1 0.5 0.8 1 0.5 Die Size (mm) 7 6.35 5.4 10 6.35 4 Substrate Thick (mm) 0.36 0.36 0.36 0.36 0.36 0.36 Pad Dia. (mm) 0.30 0.38 0.28 0.30 0.38 0.28 Substrate Pad NSMD SMD NSMD NSMD SMD NSMD Ball Dia. (mm) 0.48 0.63 0.48 0.48 0.5 0.32 103 component has multiple components. All the components are mounted on one side of the board. The test board is made of FR-4. The test board is based on standard PCB technology with no build-up or HDI layers. The test Board is 2.95" by 7.24" by 0.042" thick Two of the test boards have been used to study the reliability of chip-scale packages and ball-grid arrays. Test board A has 10 mm ball-grid array, 0.8 mm pitch, 100 I/O. It has 10 components on one side of the board (Figure 25). Test board B includes 8mm flex-substrate chip scale packages, 0.5 mm pitch, 132 I/O (Table 6-2). The number of components varies from 6 to 10 on some of the boards. All the components are on one side of the board. For the 8 mm CSP, conventional eutectic solder, 63Sn/37Pb and lead- free solder balls 95.5Sn4.0Ag0.5Cu have been studied. Test boards A and B are made of FR-4. These test boards were based on standard PCB technology with no build-up or HDI layers. Test Board A and B was 2.95" by 7.24" by 0.042" thick. 10 mm, 100 I/O BGA 8mm 132 I/O BGA Figure 25: Interconnect array configuration for 95.5Sn4.0Ag0.5Cu and 63Sn37Pb Test Vehicles. 104 Table 6-2: Test Vehicles. 10mm 63Sn37Pb 8mm 62Sn36Pb2Ag 8mm 95.5Sn4.0Ag 0.5Cu Ball Count 100 132 132 Ball Pitch 0.8 mm 0.5 mm 0.5 mm Die Size 5 x 5 3.98 x 3.98 3.98 x 3.98 Substrate Thickness 0.5 mm 0.1 mm 0.1 mm Substrate Pad Dia. 0.3 mm 0.28 mm 0.28 mm Substrate Pad Type SMD Thru-Flex Thru-Flex Ball Dia. 0.46 mm 0.3 mm 0.3 mm 105 The test boards were subjected to a controlled drop. Repeatability of drop orientation is critical to measuring a repeatable response and to develop a training signal for statistical pattern recognition. Small variations in the drop orientation can produce vastly varying transient-dynamic board responses. Significant effort was invested in developing a repeatable drop set-up. The drop height was varied from 3 feet to 6 feet. Component locations on the test boards were instrumented with strain sensors. Strain and continuity data was acquired during the drop event using a high-speed data acquisition system at 2.5 to 5 million samples per second. The drop-event was simultaneously monitored with ultra high-speed video camera operating at 50,000 frames per second. Targets were mounted on the edge of the board to allow high-speed measurement of relative displacement during drop. The test boards were dropped in their vertical orientation with a weight attached to its top edge (Figure 26). The board orientation during drop has been maintained to be close to zero degrees with the vertical. In addition, the boards were dropped in the horizontal orientation per the JESD22-B111. Strain, displacement, orientation angle, velocity, acceleration, and continuity data has been acquired simultaneously. An image tracking software was used to quantitatively measure displacements during the drop event. Figure 26 shows a typical angle, and relative displacement plot measured during the drop event. The position of the vertical line in the plot represents the present 106 Figure 26 Measurement of Velocity, Acceleration, and Relative Displacement During Impact. Figure 27 Relative Displacement and Strain Measurement in Horizontal Orientation. 107 Figure 28 Transient Strain-History at Location of CSP during Drop-Event. 108 location of the board (i.e. just prior to impact in this case) in the plot with ?pos (m)? as the ordinate axis. The plot trace subsequent to the white scan is the relative displacement of the board targets w.r.t. to the specified reference. Figure 27 shows the board instrumentation for strain and relative displacement during horizontal JEDEC drop. In addition to relative displacement, velocity, and acceleration of the board prior to impact was measured. This additional step was necessary since, the boards were subjected to a controlled drop, in order to reduce variability in drop orientation. The measured velocity prior to impact was used to correlate the controlled drop height to free-drop height ( 2ghv = ). Thus velocity prior to impact for a 6ft drop (?1.83 meter) will be 5.99 m/s. 6.2 Training of the Predictive Model Repeatability of drop orientation is critical to measuring a repeatable response and to develop a training signal for statistical pattern recognition. Small variations in the drop orientation can produce vastly varying transient-dynamic board responses. To train the predictive model the test board were dropped and the strain signals obtained when no failure occurred in the board were compared using the four statistical pattern recognition technique. The strain signals obtained during these repeatable drops as shown in Figure 29 have been used to train the model to perform prognostics and damage monitoring. The feature vectors and the Confidence Values obtained by applying the Wavelet Packet transform, the Mahalanobis Distance measure, the FFT Frequency band energy computation and the Time frequency analysis Moment calculation have been shown in Figure 30, Figure 31, Figure 32, Figure 33, Figure 34, Figure 35, and Figure 36, Figure 37, Figure 38, respectively. 109 Repeatable Test Drop Strains -2000 -1500 -1000 -500 0 500 1000 1500 -0.001 0.004 0.009 0.014 0.019 0.024 0.029 0.034 Time (sec) Strain (microstrains) drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 29 Strain data for Repeatable Drops of an electronic Assembly. 110 Wavelet Packet Energy Feature Vectors 0 0.5 1 1.5 2 2.5 3 0 20 40 60 80 100 120 140 Packet Number Packet E n ergy drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 30 Repeatable Feature Signatures obtained using Wavelet Packet Energy Vectors. 111 Wavelet Packet Analysis 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1234567 Drop Number Confidence Value Figure 31 Confidence Values obtained by applying Wavelet Packet Energy Approach to Repeatable Drops (No Failure). 112 Mahalanobis Distance Feature Vector 0 2 4 6 8 10 12 14 16 0 5000 10000 15000 20000 25000 30000 35000 Sample Number Maha lanobis Dista n ce drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 32 Repeatable Feature Signatures obtained using Mahalanobis Distance Vectors. 113 Mahalanobis Distance Analysis 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1234567 Drop Number Confidence Value Figure 33 Confidence Values obtained by applying Mahalanobis Distance computation to Repeatable Drops (No Failure). 114 FFT Frequency Band Energy Feature Vector 0.E+00 1.E+06 2.E+06 3.E+06 4.E+06 5.E+06 6.E+06 7.E+06 8.E+06 0 100 200 300 400 500 Frequency En erg y drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 34 Repeatable Feature Signatures obtained using FFT Frequency Bands Energy Vectors. 115 FFT Frequency Band Analysis 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1234567 Drop Number Confidence Value Figure 35 Confidence Values obtained by applying FFT Frequency Band Energy computation to Repeatable Drops (No Failure). 116 Time Frequency Analysis -1.000E+08 0.000E+00 1.000E+08 2.000E+08 3.000E+08 4.000E+08 5.000E+08 6.000E+08 7.000E+08 8.000E+08 0 200 400 600 800 1000 1200 Moment Number Time Moment drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 36 Repeatable Feature Signatures obtained using Time Moment Vectors. 117 Time Frquency Analysis -5.E+02 2.E+11 4.E+11 6.E+11 8.E+11 1.E+12 1.E+12 1.E+12 2.E+12 2.E+12 2.E+12 0 200 400 600 800 1000 Moment Number Freque ncy Moment drop 1 drop 2 drop 3 drop 4 drop 5 drop 6 Figure 37 Repeatable Feature Signatures obtained using Frequency Moment Vectors. 118 Time Frequency Moments 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 12 Drop Number Confidence Value Frequency Moments Time Moments Figure 38 Confidence Values obtained by applying Time Frequency Analysis to Repeatable Drops (No Failure). 119 6.3 Closed Form Model Results Closed form models are used to study the damage progression in electronic devices at product-level. Damage progression has been monitored through correlating changes in structural parameters with the dynamic response of the assembly. Effective parameter changes implying damage in an electronic assembly include changes in flexural rigidity, changes in effective properties, and the change in boundary conditions. Partial release or complete failure of constraints may produce change in the aspect ratio or eventual shift of boundary conditions of the assembly. Further, failure of the interconnects or fall-off of the components may produce changes in the flexural rigidity and smeared properties of the PCA. Case study has been analyzed using closed-form models, in which the PCB is press fitted in a product casing, which might open due to shock and drop causing the boundary conditions for vibration to change from clamped to free conditions. Clamped condition can be realized in a product with a printed circuit assembly in which the printed circuit assembly is held between two snap-fit housings. In the study, various combinations of change in boundary conditions and effective parameters have been studied in the form of case-studies. In the case-study, the boundary conditions gradually change to from the original assembly condition to a faulty condition coupled with effective parametric changes. 6.3.1 CFFF to FFFF Boundary Condition change with change in aspect ratio Partial release or complete failure of constraints may produce change in the aspect ratio or eventual shift of boundary conditions of the assembly. Further, failure of the interconnects or fall-off of the components may produce changes in the flexural rigidity 120 and smeared properties of the PCA. Case study has been analyzed using closed-form models, in which the PCB is press fitted in a product casing, which might open due to shock and drop causing the boundary conditions for vibration to change from clamped to free conditions. Clamped condition can be realized in a product with a printed circuit assembly in which the printed circuit assembly is held between two snap-fit housings. In the example case-study, the boundary conditions gradually change to FFFF, from CFFF in the original assembly. The confidence value degradation for change in boundary conditions is quite significant as shown in Figure 39 and Figure 40. Table 6-1 The Case Study Parameters. Parameter Initial Conditions After Drop and Shock Boundary Condition CFFF FFFF Aspect Ratio (a/b) 7/3 Compared for up to 10% change Flexural Rigidity 12.386 N-m 2 No Change Density 1800 kg/m 3 No Change Poisson?s ratio 0.3 No Change 121 Mode 1 0 0.2 0.4 0.6 0.8 1 1.2 02468 Percentage Change in Aspect Ratio (%) Confiden ce Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 39 Confidence Value Degradation with Change in Aspect Ratio for Mode 1. 122 Mode 2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 02468 Percentage Change in Aspect Ratio (%) Confidence Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 40 Confidence Value Degradation with Change in Aspect Ratio for Mode 2. 123 6.3.2 Point Mass Fall off from Assembly corresponding to Package Falloff The location of point masses on the plate has been shown to cause a significant change in the vibration frequencies and mode shapes of an assembly. To fully understand the dynamic characteristics of the affect of the presence of point mass components on the board, the pristine configurations of the board have been perturbed to simulate failure corresponding to package fall off and the damage progression has been monitored using SPR techniques. The affect of component fall off, i.e. point mass removal with respect to the location of the point mass has been studied. The assembly as described in the section 5.5 on point mass modeling is shown in Figure 41 and the various point masses on the assembly have been labeled using numbers. The point masses have been removed one at a time and the vibration characteristics of the perturbed assembly has been simulated to study the sensitivity of the assembly to the location of the failure. The damage monitoring based on the confidence value graph obtained in this study is shown in Figure 42. The sensitivity of failure detection irrespective to the sensor location has been studied and shown in Figure 43, the package fall off occurs at location 1 and the displacement is monitored at each package location i.e. the possible sensor locations. The point mass assembly has also been used to study the affect of the removal of a number of point masses from the assembly to study the affect on the modeshapes. As shown in Figure 44 the removal of two point masses from the assembly causes a significant change in Mode shapes of the electronic assembly. 124 Loc 1 Loc 2 Loc 3 Loc 4 Loc 5 Loc 10 Loc 9 Loc 8 Loc 7 Loc 6 Figure 41 Point Mass Closed Form Model and Numbering of Location of Packages. 125 Degradation in Mode Shape 1 with respect to Package Fall off Location 0 0.2 0.4 0.6 0.8 1 0246810 Package Fall off Location Confidence Value Wavelet Packet Analysis Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 42 Degradation in Confidence Value with respect to Location of Package Fall off. 126 Sensitivity of Package Fall off Irrespective of Sensor Placement 0 0.2 0.4 0.6 0.8 1 1.2 0246810 Package Location at which Signal Measured Confidence Value Wavelet Packet Analysis Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 43 Degradation in Confidence Value with Package Fall off from Location 1. 127 0 0.02 0.04 0.06 0 0.05 0.1 0.15 -15 -10 -5 0 5 10 15 Width Mode 3 Length 0 0.02 0.04 0.06 0 0.05 0.1 0.15 -15 -10 -5 0 5 10 15 Width Mode 3 Length 2 Packages Missing Package Missing Packages Missing Figure 44 Effect of Package Fall off on Modeshape of Assembly. 128 6.4 Model Based Correlation of Damage The closed form modeling approach described above shows us the affect of the change in effective parameters on the reliability of the confidence values. To provide a physical relevance to the above approach an explicit finite element model for the free drop and horizontal drop of a 0.5 mm pitch, 132 I/O, 8 mm flex-substrate CSP has been created. A reduced integration shell elements (S4R) is used for the PCB, and the various component layers such as the substrate, die attach, silicon die, mold compound have been modeled with C3D8R elements. The interconnects are modeled using two-node beam elements (B31) in place of solder balls as shown in Figure 46.and Figure 46 Smeared properties have been derived for the CSP considering all the individual components mentioned above. The concrete floor has been modeled using rigid R3D4 elements. A weight has been attached on the top edge of the board. The explicit models created for the study for the Vertical free drop and the Horizontal drop are shown in Figure 47and Figure 48 respectively. Some of the common faults that occur in an electronic assembly due to a drop and shock event have been modeled, and by applying the Statistical Pattern recognition techniques the degradation in confidence value is physically correlated to the occurrence of damage in an assembly. The faults simulated in this study are Solder ball cracking, solder ball failure, chip cracking, chip delamination and Package Fall off. The faults have been simulated for both the vertical and horizontal drops. 129 Figure 45 Package with Solder Beam Array. Figure 46 Solder Bam Array modeled to represent Solder Balls. 130 Detailed Package Packages based on calculated Smeared Properties Figure 47 Vertical Drop Model developed for the Study. Figure 48 Horizontal Drop Model developed for the Study. 131 6.4.1 Solder Ball Cracking and Failure The above described model for the free drop was analyzed several times, with all solder beams intact and with the various corner solder beams damaged (cracked) and failed. The statistical pattern recognition methods described in this study have been applied to the time history output of the strain signal obtained at the PCB surface below the centre of the package, where the sensor would have been mounted in an experimental setup. The solder beam array in the failure simulations for solder ball failure is shown in Figure 49 and the various solder beams missing have been marked and shown for the models developed. The solder beam array for the damaged beam model is also shown in Figure 50 and the damaged beam can be seen at the corners of the solder array. The failure of corner solder balls has been simulated by removing the corner interconnects as the corner solder balls have the most stress concentration and hence are mostly the first to fail in a solder array. The cracking of the solder ball has been simulated by reducing the cross sectional area of the corner solder beams and the model has been simulated for up to all four corner ball damaged, for both vertical and horizontal drop. The confidence values computed for solder ball failure are shown in Figure 51, for a vertical drop and in Figure 52, for a horizontal drop orientation. The damage monitoring of solder ball damage is shown in Figure 53, for a vertical drop and in Figure 54, for a horizontal drop orientation. The confidence value shows a drop in confidence with the degradation of reliability, thus showing the applicability of the above stated damage monitoring methods to the reliability studies of Electronic assemblies. 132 (a) One Interconnect Missing (b) Two Interconnects Missing (c) Three Interconnects Missing (d) Four Interconnects Missing Figure 49 Model Configurations for Correlation of Interconnect Failure to Confidence Value Degradation. 133 (a) One Interconnect Damaged (b) Two Interconnects Damaged (c) Three Interconnects Damaged (d) Four Interconnects Damaged Figure 50 Model Configurations for Correlation of Interconnect Damage to Confidence Value Degradation. 134 Damage Monitoring for a Vertical Drop 0.0 0.2 0.4 0.6 0.8 1.0 01234 Number of Corner Interconnect Failure Confidence Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 51 Confidence Value degradation in Transient PCB Strain with Solder Ball Failure for Vertical Drop. 135 Damage Monitoring for a Horizontal Drop 0 0.2 0.4 0.6 0.8 1 01234 Number of Corner Interconnect Failure Confidence Value Wavelet Packet Analysis Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 52 Confidence Value degradation in Transient PCB Strain with Solder Ball Failure for Horizontal Drop. 136 Damage Monitoring for a Vertical Drop 0 0.2 0.4 0.6 0.8 1 1234 Number of Corner Interconnects Damaged Confidence Value Wavelet Packet Energy Mahalanobis Distance Time Moments Frequency Moments Figure 53 Confidence Value degradation in Transient PCB Strain with Solder Ball Damage for Vertical Drop. 137 Damage Monitoring for a Horizontal Drop 0 0.2 0.4 0.6 0.8 1 01234 Number of Corner Interconnects Damaged Confidence Value Wavelet Packet Analysis Mahalanobis Distance FFT Frequency Band Energy Time Moments Frequency Moments Figure 54 Confidence Value degradation in Transient PCB Strain with Solder Ball Damage for Horizontal Drop. 138 6.4.2 Chip Fracture The model has also been modified to represent the cracking of the silicon chip due to drop and shock of the electronic assembly. The cracked surface on the chip is represented by detaching the chip and creating a contact surface representing the chip fracture between two parts of the detached chip. The chip fracture and the contact surface representing the chip crack has been shown in Figure 55. Statistical pattern recognition is applied to the time history output of the strain signal obtained at the PCB surface below the centre of the package, where the sensor would have been mounted in an experimental setup. The confidence value obtained for the vertical drop and the horizontal drop orientation has been plotted in Figure 56 and Figure 57 respectively, and shows the expected degradation in confidence value with the fracture of the chip in the assembly. 6.4.3 Chip Delamination Shock and drop of an assembly can also cause delamination between the chip and the substrate, thus causing failure in the assembly. The delamination is modeled by detaching part of the substrate from the chip. A contact surface is then created to represent the delaminated surface between the substrate and the chip attach. The delaminated chip modeled by the contact surface between the chip and the substrate has been shown in Figure 58. The strain is obtained at the PCB surface below the centre of the package, and statistical pattern recognition is applied to the obtained signals. The confidence value 139 drop obtained for the horizontal drop orientation and the vertical drop orientation is shown in Figure 59 and Figure 60 respectively. Contact surface at crack location Figure 55 Model configuration for Chip Fracture (cracking). 140 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Vertical Drop Uncracked Chip Cracked Chip Confidence Value Figure 56 Confidence Value degradation in Transient PCB Strain with Chip Failure for Vertical drop orientation. 141 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Horizontal Drop Uncracked Chip Cracked Chip Confidence Value Figure 57 Confidence Value degradation in Transient PCB Strain with Chip Failure for Horizontal drop orientation. 142 Contact Surface representing Delamination Figure 58 Model configuration for Chip Delamination. 143 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Vertical Drop No Delamination Delamination Confidence Value Figure 59 Confidence Value degradation in Transient PCB Strain with Chip Delamination for Vertical drop orientation. 144 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Horizontal Drop No Delamination Delamination Confidence Value Figure 60 Confidence Value degradation in Transient PCB Strain with Chip Delamination for Horizontal drop orientation. 145 6.4.4 Package Fall Off The loss of package from the board is also a scenario encountered in the drop and shock testing of electronic assemblies, and thus has been considered. The finite element explicit model has been developed for the case of a package fall off by removing one package from the assembly, as simulated using closed form models in section 6.3.2. The model developed for failure, the removal of the package from the assembly to simulate failure has been shown in Figure 61. The failure model has been developed for both the vertical drop and the horizontal drop. The strain is obtained at the PCB surface below the centre of the package falling off, where the sensor would have been mounted in an experimental setup, and statistical pattern recognition is applied to the obtained signals. The confidence value drop obtained in the vertical drop failure simulation is shown in Figure 62 . As would be expected, as soon as the package falls off the assembly the confidence value drops to zero, thus showing the applicability of the statistical pattern recognition techniques for damage monitoring in electronic assemblies. The health degradation due to package fall off in the horizontal model can be seen for the horizontal drop in Figure 63. 6.5 Experimental Validation The damage monitoring of electronic assemblies by statistical pattern recognition has been applied to experimental results obtained by the JESD22-B11 drop and shock testing of PCB011. The boards were thermal cycled for 750 cycles and then subjected to JEDEC drop testing. The strain signal obtained from sensors placed on the board at appropriate locations are employed to perform statistical pattern recognition. 146 (a) (b) Figure 61 Model configuration for loss of package from assembly (a) Vertical Drop (b) Horizontal Drop. 147 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Vertical Drop No Failure Package Fall off Co nf id en ce Value Figure 62 Confidence Value degradation in Transient PCB Strain with Package Loss for Vertical drop orientation. 148 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelet Packet Mahalanobis Distance FFT Energy Time Moments Frequency Moments Damage Monitoring for Horizontal Drop No Failure Package Fall off Co nf id en ce Value Figure 63 Confidence Value degradation in Transient PCB Strain with Package Loss for Horizontal drop orientation. 149 The feature vectors obtained using the Wavelet packet analysis, the Mahalanobis distance computation, the FFT frequency band analysis and the time moments and frequency moment distribution are shown in Figure 64, Figure 65, Figure 66, Figure 67, and Figure 68 respectively. As shown the feature vectors used in failure pattern classification are seen to show significant change in case of failure of a package irrespective to the placement of the sensor. Statistical pattern recognition has been applied to the strain signals taken at the various drops of the board. A linear regression fit of the confidence values obtained show a progressive degradation of reliability of the assembly with the number of drops. A significant drop of reliability occurs with failure of individual packages on the assembly. The confidence value plots, as shown in Figure 69 and Figure 70, computed by statistical pattern recognition of strain signals obtained from the strain sensors show a good correlation of the reliability degradation with the experimental values. The above correlation is performed to exhibit the applicability of statistical pattern recognition techniques to monitor damage progression in experimental setups. The degradation in confidence values demonstrates the capability of the approach in sensing damage progression and failure at the same location and other locations in the board assembly. Package-3 transient strain shows degradation in confidence value because of failure at package location-1. Package-1 transient strain shows degradation in confidence value due to failure at package location-1 and package-location-3. 150 Feature Vector At Drop 1 and at Failure Drop 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2040608010 Packet Number Ener gy Vector drop 1 drop 63 Figure 64 Wavelet Packet Energy Feature Vector used in Failure Classification. 151 Distance Feature Vectors at Drop1 and Failure Drop 0 10 20 30 40 50 60 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 time (sec) Ma hala nobi s Distance drop 1 drop 63 Figure 65 Mahalanobis Distance Feature Vector used for Failure Classification. 152 FFT Frequency Band Energy Feature Vector at Drop 1 and Failure Drop 0 1E+11 2E+11 3E+11 4E+11 5E+11 6E+11 7E+11 8E+11 9E+11 0 200 400 600 800 Frequency (Hz) Band Energy drop 1 drop 63 Figure 66 FFT Frequency Band Energy Feature Vector used for Failure Classification. 153 Time Moment Feature Vector 0 1000 2000 3000 4000 5000 6000 0 0.01 0.02 0.03 0.04 0.05 Time (sec) Instantane ous Frequency (Hz) drop 1 drop 63 Figure 67 Time Moment Feature Vector used for Failure Classification. 154 Frequency Moment Feature Vector -5 -3 -1 1 3 5 7 9 11 13 15 0 100 200 300 400 Frequency (Hz) Instanaeous Time (sec) drop1 drop 63 Figure 68 Frequency Moment Feature Vector used for Failure Classification. 155 Damage Degradation (Sensor under Package-1) 0 0.2 0.4 0.6 0.8 1 1.2 0 50 100 150 200 Drop Number Con fidence Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moment Frequency Moment Package-1 Fails Package-2 Fails Package-3 Fails Package-4 Fails Con fidence Value Figure 69 Confidence value degradation showing progressive damage with the Drops. 156 Damage Degradation (Sensor Under Package-3) 0 0.2 0.4 0.6 0.8 1 1.2 0 50 100 150 200 Drop Number Confidence Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moment Frequency Moment Package-1 Fails Package-2 Fails Package-3 Fails Package-4 Fails Confidence Value Figure 70 Confidence value degradation showing progressive damage with the Drops. 157 6.6 Solder Joint Built in Reliability Test (SJ-BIRT) Solder joints in electronic assemblies subjected to shock and drop are prone to failure due to high strain rate transient bending produced by such loads. The damage in the solder joint occurs due to overstress or cumulative fatigue and occurs in the form of plastic work and cracks in the solder balls. The Built in self test (BIST) approach is currently being applied to the testing of Digital chips and systems [Hashempour et.al. 2004], Internal and External Memories like DDR, QDR, Double DDR SRAM, FCRAM, and RLDRAM [Kim et.al 2004], ASICs [Sato et.al. 2001], and Mixed-Signal BIST applied on devices like ADCs, DACs, filters, amplifiers, power regulators, mixers [Sunter 2002, Huang et.al. 2000]. Built-In self tests have also been applied to the functional testing of FPGAs, testing the core logic block, the memory and various interconnect faults [Dutt 2006, Abramovici et.al. 2004, Liu et.al. 2003, Sun et.al. 2000, Stroud et.al. 1998]. The BIST approach has also been applied to testing of MEMS Accelerometers [Deb et.al. 2006] and Industrial circuits [Kiefer et.al. 2000] but the current version of BIST approach is focused on reactive failure detection and provides limited insight in to solder joint reliability and residual life. The stress magnitudes occurring in the solder is difficult to predict and quantize and hence the prognostic approach based solely on accelerated life testing can give inaccurate life predictions [Lall 2004]. With the current testing methodologies utilizing the functional performance as a measure of health, the in-situ Solder joint Built in Reliability test (SJ-BIRT) provides prognostics even when the damaged solder joint does not cause immediate operational failure. Built in Self Tests (BIST) are applied at component levels to perform reliability tests to isolate manufacturing failures that might 158 occur, but with the use of SJ-BIRT the failure modes caused during the PCB-FPGA assembly can also be identified. The failure in a solder-joint manifests itself in the form of an increase in the resistance of the joint from milliohms to tens and hundreds of ohms. The failure might be an open circuit or might be due to the solder cracking, which results in different values of the resistance of the joint. A crack progressing with time becomes longer until it causes the solder joint to open and the device to fail completely, thus by using an in-situ BIRT we can predict the degradation of the solder joint reliability. The SJ BIRT is performed by attaching a small capacitor to the I/O port and by programming the FPGA to write a voltage to the capacitor through a solder joint being tested and reading the voltage from the another solder joint. A prototype circuit has been modeled and analyzed in PSPICE to study the behavior of a damaged solder interconnect compared to an undamaged interconnect as shown in Figure 71. The undamaged solder interconnect is modeled as a resistance having a value to 20 milliohms while a failed interconnect is modeled as a 20 ohms. The voltage across the 1?F capacitor is measured and observed to identify failures in the device. The voltage characteristics of the capacitor are shown below in Figure 72 for both damaged and undamaged interconnects. The basic algorithm behind the SJ BIRT technique is the writing of a logical high i.e. ?1? to charge the capacitor and then reading the voltage across the charged capacitor. The capacitor gets fully charged when the solder interconnect is undamaged and a logical high ?1? is read by the BIRT. When due to the high resistance produced by the damaged interconnect, the RC time constant becomes high and the capacitor does not get fully charged, a logical low i.e. ?0? is read by the SJ BIRT. 159 The voltage signals obtained from the SJ BIRT circuit has been analyzed for various values of the resistance value and statistical pattern recognition has been applied to these voltage signals for prognostics and health monitoring of the device. The application of statistical pattern recognition provides a quantified damage index for the reliability and performance of the FPGA as even though there might be damage in the device, the SJ BIRT might get a logical high of ?1? if the damage is not complete failure and the resistance value hasn?t increased enough to cause the capacitor not to charge to the threshold set for the testing. The confidence value graphs shown in Figure 73 show that the above outlined method can be applied successfully to the prognostics of FPGA based on a SJBIST approach. Figure 71 Solder Joint Built in Reliability test Circuit Design. 160 Voltage Characteristics 0 1 2 3 4 5 6 100 110 120 130 140 150 Time (nanosec) V o lat g e acr o ss C a pacitor (V olts ) 0.02 ohms (Undamaged) 0.2 ohms 2 ohms 20 ohms 100 ohms Figure 72 Voltage Characteristics obtained due to variation in Solder interconnect resistance due to heath degradation. 161 SJ-BIRT Confidence Value 0 0.2 0.4 0.6 0.8 1 1.2 0.01 0.1 1 10 100 Resistance Value (Logarithmic Scale) Confidence Value Wavelet Packet Energy Mahalanobis Distance FFT Frequency Band Energy Time Moment Frequency Moment Figure 73 Damage Detection using a Solder Joint Built in Reliability Test (SJ-BIRT). 162 CHAPTER 7 SUMMARY & CONCLUSIONS In this work an approach for health monitoring in electronic products, based on closed-form energy-method based models, explicit finite elements, and statistical pattern recognition has been developed. Statistical pattern recognition techniques previously employed in several engineering and scientific disciplines such as biology, psychology, medicine, marketing, artificial intelligence, computer vision and remote sensing, have been used for monitoring damage progression in electronic assemblies. Damage proxies have been developed based on the Wavelet Packet Energy, the Mahalanobis Distance approach, the FFT Frequency Band energy and the Time Frequency Analysis methods and applied to the experimental and simulation data. The above approach addresses detection and monitoring of shock-even damage, without requiring continuous high-speed interconnect resistance monitoring during drop and shock events. Detected changes in damage have been demonstrated with changes in the energy feature vector and the change in the confidence value of the signal. The approach has been demonstrated for several test cases including �? Failure and damage of one-or-more interconnects in an area array package �? Chip Delamination �? Chip cracking 163 �? Package Fall off �? failure of packages at the location of transient strain measurement or at another location �? change in the structural properties and the transient dynamic response of the structure. The various damage proxies have been developed by simulating the studied faults in the explicit finite element models and the closed form analytical models developed for the study. The confidence value is shown to quantify the reliability degradation and provides an effective measure for the occurrence of a fault in the assembly. As shown in Figure 74 the drop in confidence value is directly related to the amount of damage that occurs in the assembly. A Solder joint Built in Reliability Test (SJ-BIRT) has been studied and statistical pattern recognition has been applied to the voltage signals obtained from the test. 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