Survey on data-driven industrial process monitoring and diagnosis

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1224 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)220-234
Journal / PublicationAnnual Reviews in Control
Volume36
Issue number2
Online published26 Oct 2012
Publication statusPublished - Dec 2012
Externally publishedYes

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.

Research Area(s)

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