VisualBlock-FIR for fault detection and identification: application to the DAMADICS benchmark problem

  • Authors:

  • Antoni Escobet;Àngela Nebot;François E. Cellier

  • Affiliations:

  • Dept. ESAII, Universitat Politècnica de Catalunya, Manresa, Spain;Dept. LSI, Universitat Politècnica de Catalunya, Barcelona, Spain;Institute of Computational Science, ETH Zurich, Zurich, Switzerland

  • Venue:
  • MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
  • Year:
  • 2007

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Abstract

This paper describes a fault diagnosis system (FDS) for non-linear plants based on fuzzy logic. The proposed scheme, named VisualBlock-FIR, runs under the Simulink framework and enables early fault detection and identification. During fault detection, the FDS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identification, the FDS should conclude which type of failure has occurred. The enveloping and acceptability measures introduced in VisualBlock-FIR enhance the robustness of the overall process. The final part of this research shows how the proposed approach is used for tackling faults of the DAMADICS benchmark.