Statistical process control via context modeling of finite-state processes: An application to production monitoring

Irad Ben-Gal, Gonen Singer

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output.

Original languageEnglish
Pages (from-to)401-415
Number of pages15
JournalIIE Transactions (Institute of Industrial Engineers)
Volume36
Issue number5
DOIs
StatePublished - May 2004
Externally publishedYes

Bibliographical note

Funding Information:
This research was partially supported by the MAGNET/ CONSIST Grant.

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