One-class classification-based control charts for monitoring autocorrelated multivariate processes

Seoung Bum Kim, Weerawat Jitpitaklert, Thuntee Sukchotrat

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T2 chart.

Original languageEnglish
Pages (from-to)461-474
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume39
Issue number3
DOIs
Publication statusPublished - 2010 Mar 1

Fingerprint

One-class Classification
Statistical process control
Control Charts
Monitoring
Statistical Process Control
Hotelling's T2
Violate
Chart
Prediction Model
Control charts
Consecutive
Monitor
Manufacturing
Necessary
Dependent

Keywords

  • Autocorrelated multivariate process
  • Data mining algorithm
  • One-class classification algorithm
  • Statistical process control

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability

Cite this

One-class classification-based control charts for monitoring autocorrelated multivariate processes. / Kim, Seoung Bum; Jitpitaklert, Weerawat; Sukchotrat, Thuntee.

In: Communications in Statistics: Simulation and Computation, Vol. 39, No. 3, 01.03.2010, p. 461-474.

Research output: Contribution to journalArticle

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