Profile Monitoring of Multivariate Processes for Efficient Detection of Parameter Changes
Type of DegreePhD Dissertation
DepartmentIndustrial and Systems Engineering
MetadataShow full item record
Profile monitoring has been extensively studied when the profile of a quality characteristic is normally distributed. There are a limited number of studies for the case when a profile follows other distributions such as Weibull, lognormal or Gamma. A profile, having these last three distributions, has many practical applications. It is also of interest to determine how well profile-monitoring of estimators can detect changes in parameters of an underlying distribution. Control chart methods are utilized to monitor such parameter estimates. The performance of a few monitoring statistics is investigated in this dissertation. A form of the Hotelling’s T2 statistic and another that utilizes the concept of an exponentially weighted moving average are investigated. As a performance measure, the mean and standard deviation of the time to first detection of shift in process parameters, are explored. We also investigated a method that utilizes estimated percentiles of the distribution for profile monitoring.
- Dissertation - Mishra_Final.pdf