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NONPARAMETRIC MULTIVARIATE STATISTICAL PROCESS CONTROL USING PRINCIPAL COMPONENT ANALYSIS AND SIMPLICIAL DEPTH

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Date Issued:
2006
Abstract/Description:
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.
Title: NONPARAMETRIC MULTIVARIATE STATISTICAL PROCESS CONTROL USING PRINCIPAL COMPONENT ANALYSIS AND SIMPLICIAL DEPTH.
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Name(s): Beltran, Luis, Author
Malone, Linda, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2006
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0001065 (IID), ucf:46792 (fedora)
Note(s): 2006-05-01
Ph.D.
Engineering and Computer Science, Department of Industrial Engineering and Management Systems
Doctorate
This record was generated from author submitted information.
Subject(s): Multivariate Statistical Process Control
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0001065
Restrictions on Access: campus 2007-01-31
Host Institution: UCF

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