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

Nong Ye, Darshit Parmar, Connie M. Borror

Research output: Contribution to journalArticle

20 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 languageEnglish (US)
Pages (from-to)393-402
Number of pages10
JournalQuality and Reliability Engineering International
Volume22
Issue number4
DOIs
StatePublished - Jun 1 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

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