Causation-based T 2 decomposition for multivariate process monitoring and diagnosis

Jing Li, Jionghua Jin, Jianjun Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Successful application of SPC for monitoring and diagnosis in complex manufacturing systems is a challenging problem due to the excessive number of process/product variables and their intricate relationships. The T 2 decomposition approach [7] (called MTY approach), although popularly used, may receive concerns on its computational efficiency and capability of reliably identifying the root causes of the fault in a large complex system. This paper proposes a causation-based T 2 decomposition approach by effectively incorporating causal models into the MTY approach. Theoretical analysis and simulation studies demonstrate that the proposed causation-based T 2 decomposition has enhanced interpretability and diagnostic power.

Original languageEnglish (US)
Title of host publication2006 IIE Annual Conference and Exhibition
StatePublished - 2006
Externally publishedYes
Event2006 IIE Annual Conference and Exposition - Orlando, FL, United States
Duration: May 20 2006May 24 2006

Other

Other2006 IIE Annual Conference and Exposition
CountryUnited States
CityOrlando, FL
Period5/20/065/24/06

Keywords

  • Causal models
  • Monitoring and diagnosis
  • SPC
  • T decomposition

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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    Li, J., Jin, J., & Shi, J. (2006). Causation-based T 2 decomposition for multivariate process monitoring and diagnosis. In 2006 IIE Annual Conference and Exhibition