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

113 Citations (Scopus)

Abstract

In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector AR model with wavelet based denoising. Given the fault direction, a new index is proposed to detect the fault, which integrates fault detection and prognosis together. Case studies on a continuous stirred tank reactor and the Tennessee Eastman process demonstrate the effectiveness of the proposed approaches. © 2010 Elsevier Ltd.
Original languageEnglish
Pages (from-to)1211-1219
Number of pages9
JournalControl Engineering Practice
Volume18
Issue number10
Early online date20 Jun 2010
DOIs
Publication statusPublished - Oct 2010
Externally publishedYes

Keywords

  • Multivariate fault prognosis
  • Principal component analysis
  • Reconstruction based estimation
  • Vector AR model
  • Wavelet based denoising

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