Survey on data-driven industrial process monitoring and diagnosis

S. Joe QIN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1227 Citations (Scopus)

Abstract

This paper provides a state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades. The scope of the problem is described with reference to the scale and complexity of industrial process operations, where multi-level hierarchical optimization and control are necessary for efficient operation, but are also prone to hard failure and soft operational faults that lead to economic losses. Commonly used multivariate statistical tools are introduced to characterize normal variations and detect abnormal changes. Further, diagnosis methods are surveyed and analyzed, with fault detectability and fault identifiability for rigorous analysis. Challenges, opportunities, and extensions are summarized with the intent to draw attention from the systems and control community and the process control community. © 2012 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)220-234
Number of pages15
JournalAnnual Reviews in Control
Volume36
Issue number2
Early online date26 Oct 2012
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes

Bibliographical note

Financial support for this work from sponsors of the Texas–Wisconsin–California Control Consortium and the Fundamental Research Funds for the Central Universities, No. RC1101 is gratefully acknowledged.

Keywords

  • Fault detection
  • Fault diagnosis
  • Machine learning
  • Partial least squares
  • Principal component analysis
  • Quality monitoring
  • Statistical process monitoring

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