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Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

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  • Hacene Habbi
  • Madjid Kidouche
  • Michel Kinnaert
  • Mimoun Zelmat

Abstract

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

Suggested Citation

  • Hacene Habbi & Madjid Kidouche & Michel Kinnaert & Mimoun Zelmat, 2011. "Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(4), pages 587-599.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:4:p:587-599
    DOI: 10.1080/00207721003653666
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    Cited by:

    1. Andrés Bustillo & Juan J. Rodríguez, 2014. "Online breakage detection of multitooth tools using classifier ensembles for imbalanced data," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2590-2602, December.

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