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Ensemble-Bayesian SPC: Multi-mode process monitoring for novelty detection

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  • Marcelo Bacher
  • Irad Ben-Gal

Abstract

We propose a monitoring method based on a Bayesian analysis of an ensemble-of-classifiers for Statistical Process Control (SPC) of multi-mode systems. A specific case is considered, in which new modes of operations (new classes), also called “novelties,” are identified during the monitoring stage of the system. The proposed Ensemble-Bayesian SPC (EB-SPC) models the known operating modes by categorizing their corresponding observations into data classes that are detected during the training stage. Ensembles of decision trees are trained over replicated subspaces of features, with class-dependent thresholds being computed and used to detect novelties. In contrast with existing monitoring approaches that often focus on a single operating mode as the “in-control” class, the EB-SPC exploits the joint information of the trained classes and combines the posterior probabilities of various classifiers by using a “mixture-of-experts” approach. Performance evaluation on real datasets from both public repositories and real-world semiconductor datasets shows that the EB-SPC outperforms both conventional multivariate SPC as well as ensemble-of-classifiers methods and has a high potential for novelty detection including the monitoring of multimode systems.

Suggested Citation

  • Marcelo Bacher & Irad Ben-Gal, 2017. "Ensemble-Bayesian SPC: Multi-mode process monitoring for novelty detection," IISE Transactions, Taylor & Francis Journals, vol. 49(11), pages 1014-1030, November.
  • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:11:p:1014-1030
    DOI: 10.1080/24725854.2017.1347984
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    Cited by:

    1. Douek-Pinkovich, Yifat & Ben-Gal, Irad & Raviv, Tal, 2022. "The stochastic test collection problem: Models, exact and heuristic solution approaches," European Journal of Operational Research, Elsevier, vol. 299(3), pages 945-959.
    2. Yuval Cohen & Gonen Singer, 2021. "A smart process controller framework for Industry 4.0 settings," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1975-1995, October.

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