Fault detection and isolation of faults in a multivariate process with Bayesian network

Sylvain Verron, Jing Li, Teodor Tiplica

Research output: Contribution to journalArticle

64 Scopus citations

Abstract

The main objective of this paper is to present a new method of detection and isolation with a Bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T2 statistic. The second one is a previous work on the detection of fault with Bayesian networks [2], notably on the modeling of multivariate control charts in a Bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing to decide if a fault has appeared in the process. This structure permits the isolation of the variables implicated in the fault. A particular interest of the method is the fact that the detection and the isolation can be made with a unique tool: a Bayesian network.

Original languageEnglish (US)
Pages (from-to)902-911
Number of pages10
JournalJournal of Process Control
Volume20
Issue number8
DOIs
StatePublished - Sep 2010

Keywords

  • Bayesian network
  • Multivariate SPC
  • T decomposition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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