Multivariate quality control using principal components

Susan Schall; Jeya Chandra

doi:10.1080/00207548708919862

Multivariate quality control using principal components

Susan Schall, Jeya Chandra

Marcus Department of Industrial and Manufacturing Engineering

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

In this research a method of using principal components is developed to control a process which has many output characteristics affecting the quality of the final product. The advantage of using principal components lies in the reduction of the number of variables to be control charted, thereby minimizing the combined probability of a type I error. Expressions using multivariate regression analysis are developed to predict the input variables. Numerical examples are given to illustrate the methodology developed.

Original language	English (US)
Pages (from-to)	571-588
Number of pages	18
Journal	International Journal of Production Research
Volume	25
Issue number	4
DOIs	https://doi.org/10.1080/00207548708919862
State	Published - Apr 1987

All Science Journal Classification (ASJC) codes

Strategy and Management
Management Science and Operations Research
Industrial and Manufacturing Engineering

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10.1080/00207548708919862

Cite this

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title = "Multivariate quality control using principal components",

abstract = "In this research a method of using principal components is developed to control a process which has many output characteristics affecting the quality of the final product. The advantage of using principal components lies in the reduction of the number of variables to be control charted, thereby minimizing the combined probability of a type I error. Expressions using multivariate regression analysis are developed to predict the input variables. Numerical examples are given to illustrate the methodology developed.",

author = "Susan Schall and Jeya Chandra",

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AB - In this research a method of using principal components is developed to control a process which has many output characteristics affecting the quality of the final product. The advantage of using principal components lies in the reduction of the number of variables to be control charted, thereby minimizing the combined probability of a type I error. Expressions using multivariate regression analysis are developed to predict the input variables. Numerical examples are given to illustrate the methodology developed.

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Multivariate quality control using principal components

Abstract

All Science Journal Classification (ASJC) codes

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