Keywords
Multivariate Statistical Process Control
Abstract
Although there has been progress in the area of Multivariate Statistical Process Control (MSPC), there are numerous limitations as well as unanswered questions with the current techniques. MSPC charts plotting Hotelling's T2 require the normality assumption for the joint distribution among the process variables, which is not feasible in many industrial settings, hence the motivation to investigate nonparametric techniques for multivariate data in quality control. In this research, the goal will be to create a systematic distribution-free approach by extending current developments and focusing on the dimensionality reduction using Principal Component Analysis. The proposed technique is different from current approaches given that it creates a nonparametric control chart using robust simplicial depth ranks of the first and last set of principal components to improve signal detection in multivariate quality control with no distributional assumptions. The proposed technique has the advantages of ease of use and robustness in MSPC for monitoring variability and correlation shifts. By making the approach simple to use in an industrial setting, the probability of adoption is enhanced. Improved MSPC can result in a cost savings and improved quality.
Notes
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Graduation Date
2006
Semester
Spring
Advisor
Malone, Linda
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Industrial Engineering and Management Systems
Degree Program
Industrial Engineering and Management Systems
Format
application/pdf
Identifier
CFE0001065
URL
http://purl.fcla.edu/fcla/etd/CFE0001065
Language
English
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Beltran, Luis, "Nonparametric Multivariate Statistical Process Control Using Principal Component Analysis And Simplicial Depth" (2006). Electronic Theses and Dissertations. 852.
https://stars.library.ucf.edu/etd/852