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Fault Detection and Isolation of Industrial Processes Using Optimized Fuzzy Models

  • Luis Mendonça
  • João Sousa
  • José Sá da Costa
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

3.6. Conclusions

This chapter proposed an FDI scheme based on fuzzy models. In this approach, fuzzy models (observers) are used both for normal operation and for each faulty operation. The fuzzy observers are obtained from simulated data driven by real data. The inputs of the fuzzy models are selected using the RC algorithm, and the parameters of the fuzzy models are optimised using a real-coded genetic algorithm. The FDI scheme uses these fuzzy observers to compute the residuals. The application of this approach to a pneumatic servomotor actuated industrial valve has shown its ability to detect and isolate six abrupt and six incipient faults. Note that the data contains noise, which increases the difficulty to detect and isolate the faults.

Future research will consider the extension of the proposed FDI scheme to a larger number of faults, and the inclusion of intermittent faults to be detected and isolated.

Keywords

Fault Detection Fault Diagnosis Regularity Criterion Incipient Fault Unknown Input Observer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Luis Mendonça
    • 1
  • João Sousa
    • 1
  • José Sá da Costa
    • 1
  1. 1.Dept. of Mechanical Engineering, GCAR/IDMECTechnical University of LisbonLisbonPortugal

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