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Keywords:

  • fault detection and isolation;
  • principal component analysis;
  • Gaussian mixture model;
  • contribution plots

Abstract

Contribution plots of the monitored statistics, Q and T2, are investigated to locate faulty variables when the statistics are out of their control limits. It is a popular method for fault isolation; however, it is well known that the smearing out of contributions leads to misdiagnose the faulty variables. Alternatively, the reconstruction-based contribution approach is claimed to guarantee correct diagnosis. It has been examined in this paper that the approach fails to locate faulty variables when encountering multiple sensor faults. A fault isolation chart on principal component subspace is provided to locate faulty variables for a process with multiple operating regions. The results of an industrial application show that the proposed approach locates faulty variables precisely, whereas the root causes of the abnormalities have been successfully identified. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.