Data-driven Fault Detection and Diagnosis for Complex Industrial Processes

S. Joe QIN*

*Corresponding author for this work

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

80 Citations (Scopus)


The objective of this paper is to provide a status report of the methods and applications of data-driven fault detection and diagnosis to the IFAC Safeprocess community. The scope of the problem is described with reference to the scale and complexity of industrial processes, where multi-level hierarchical optimization and control are a must for efficient operation, but are also prone to hard or soft 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 discussed next, with additional topics for rigorous analysis. An application case is presented, and future challenges and opportunities are speculated with the hope to draw more attention from the Safeprocess community. © 2009 IFAC.
Original languageEnglish
Pages (from-to)1115-1125
Number of pages11
JournalIFAC Proceedings Volumes
Issue number8
Publication statusPublished - Jun 2009
Externally publishedYes
Event7th IFAC Symposium onFault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS'09) - Barcelona, Spain
Duration: 30 Jun 20093 Jul 2009

Bibliographical note

ISBN: 9783902661463 <br/>Financial support for this work from sponsors of the Texas-Wisconsin-California Control Consortium is gratefully acknowledged.


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


Dive into the research topics of 'Data-driven Fault Detection and Diagnosis for Complex Industrial Processes'. Together they form a unique fingerprint.

Cite this