Reconstruction based fault prognosis for continuous processes

Gang LI*, S. Joe QIN, Yindong JI, Donghua ZHOU

*Corresponding author for this work

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

6 Citations (Scopus)


In this paper, a fault prognosis approach for continuous processes with hidden faults is proposed based on the principal component analysis structure and multivariate time series prediction. It is assumed that the actual fault is a slowly time-varying autocorrelated process and the fault can be completely reconstructed. Fault magnitude is estimated via reconstruction first, then predicted by a vector ARMA model. A new fault detection policy is proposed and the denoising effect on prediction modeling is studied. The case study of CSTR demonstrates the efficiency of the approach and the validity of the analysis. © 2009 IFAC.
Original languageEnglish
Pages (from-to)1019-1024
Number of pages6
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


  • Fault prognosis
  • Fault reconstruction
  • Principal component analysis
  • Vector ARMA


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