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Neuro-fuzzy identification applied to fault detection in nonlinear systems

Author

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  • L. Blázquez
  • Luis de Miguel
  • Fernando Aller
  • José Perán

Abstract

This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input–output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.

Suggested Citation

  • L. Blázquez & Luis de Miguel & Fernando Aller & José Perán, 2011. "Neuro-fuzzy identification applied to fault detection in nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(10), pages 1771-1787.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:10:p:1771-1787
    DOI: 10.1080/00207721003653674
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    Cited by:

    1. Maoyin Chen & Guibin Xu & Rongyi Yan & Steven X. Ding & Donghua Zhou, 2015. "Detecting scalar intermittent faults in linear stochastic dynamic systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(8), pages 1337-1348, June.

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