Process monitoring with principal components and partial least squares

Teri Reed Rhoads, Douglas Montgomery

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

Abstract

Statistical process monitoring in a multivariate setting is a common problem in many industries, including both continuous (chemical and process) and discrete manufacturing. Traditionally, techniques based on multivariate control charts have been suggested for this application. However, these procedures suffer several disadvantages, including insensitivity to process shifts when many process variables are simultaneously monitored, and inefficiency in determining which subsets of process variables are responsible for out-of-control signals. This paper presents a review and comparison of principal component analysis, partial least squares, and traditional control charting.

Original languageEnglish (US)
Title of host publicationIndustrial Engineering Research - Conference Proceedings
EditorsR.G. Askin, B. Bidanda, S. Jagdale
Place of PublicationNorcross, GA, United States
PublisherIIE
Pages683-686
Number of pages4
StatePublished - 1996
EventProceedings of the 1996 5th Industrial Engineering Research Conference - Minneapolis, MN, USA
Duration: May 18 1996May 20 1996

Other

OtherProceedings of the 1996 5th Industrial Engineering Research Conference
CityMinneapolis, MN, USA
Period5/18/965/20/96

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ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Rhoads, T. R., & Montgomery, D. (1996). Process monitoring with principal components and partial least squares. In R. G. Askin, B. Bidanda, & S. Jagdale (Eds.), Industrial Engineering Research - Conference Proceedings (pp. 683-686). IIE.