Integrating independent component analysis and support vector machine for multivariate process monitoring

Chun Chin Hsu*, Mu-Chen Chen, Long Sheng Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA-PCA and PCA-SVM.

Original languageEnglish
Pages (from-to)145-156
Number of pages12
JournalComputers and Industrial Engineering
Volume59
Issue number1
DOIs
StatePublished - 1 Aug 2010

Keywords

  • Fault detection rate
  • ICA
  • PCA
  • SVM
  • TE process

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