A hybrid SPC method with the chi-square distance monitoring procedure for large-scale, complex process data

Nong Ye; Darshit Parmar; Connie M. Borror

doi:10.1002/qre.717

A hybrid SPC method with the chi-square distance monitoring procedure for large-scale, complex process data

Nong Ye, Darshit Parmar, Connie M. Borror

Industrial, Systems and Operations Engineering

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

22 Scopus citations

Abstract

Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T², cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square (χ²) Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T² for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data.

Original language	English (US)
Pages (from-to)	393-402
Number of pages	10
Journal	Quality and Reliability Engineering International
Volume	22
Issue number	4
DOIs	https://doi.org/10.1002/qre.717
State	Published - Jun 2006

Keywords

Auto-correlated data
Correlated data
Hotelling's T
Non-normally distributed data
Normally distributed data
Uncorrelated data
χ Distance Monitoring

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Management Science and Operations Research

Access to Document

10.1002/qre.717

Cite this

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abstract = "Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T2, cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square (χ2) Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data.",

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AU - Parmar, Darshit

AU - Borror, Connie M.

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N2 - Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T2, cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square (χ2) Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data.

AB - Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T2, cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square (χ2) Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data.

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KW - Non-normally distributed data

KW - Normally distributed data

KW - Uncorrelated data

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A hybrid SPC method with the chi-square distance monitoring procedure for large-scale, complex process data

Abstract

Keywords

ASJC Scopus subject areas

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