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Computational Intelligence in Fault Diagnosis pp 305–334Cite as

Fuzzy Neural Networks Applied to Fault Diagnosis

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

10.5. Summary

This chapter is concerned with the application of fuzzy neural networks to fault detection and isolation systems. Thus, for readers not familiar with the subject, the background knowledge associated with artificial neural networks and the potential fields of application of this technology is presented in the introduction section. Furthermore, aiming to demonstrate that such a technology is mature enough to be applied in the solution of several kinds of industrial problems, a wide range of industrial applications of classical feedforward artificial neural networks are also reported in section 10.2, as well as applications of different types of fuzzy neural networks.

Section 10.3 is concerned with the development of FDI approaches based on fuzzy neural networks and a specific fault isolation system based on a hierarchical structure of several fuzzy neural networks is described in detail. The robustness and performance of such a fault isolation system has been assessed in section 10.4 by using a test bed consisting of a pneumatic servomotor actuated industrial control valve. Different kinds of faults have been considered, which has been assumed to occur in an abrupt or incipient manner, or by affecting the measurement variables in the process under supervision in an abrupt way or, instead, by affecting the process behaviour slowly (incipient faults).

The results presented in section 10.4 have shown that, under abrupt faults, the HSFNN provides very accurate results and is characterized by a good generalization capability as a fault isolation system. Under incipient or multiple simultaneous faulty scenarios, the performance of the proposed methodology depends on the fault development speed and/or on the system nonlinearities.

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Authors and Affiliations

  1. Mechanical Engineering Studies Centre, IDMEC/ISEL, Polytechnic Institute of Lisbon, Rua Conselheiro Emídio Navarro, 1950-062, Lisbon, Portugal

    João Calado

  2. Department of Mechanical Engineering, GCAR/IDMEC, Technical University of Lisbon, Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    José Sá da Costa

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  1. João Calado
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Editors and Affiliations

  1. Oxford University Computing Laboratory, Oxford, UK

    Vasile Palade PhD

  2. KES Center, University of South Australia, Australia

    Lakhmi Jain PhD

  3. Department of Communication Systems, Lancaster University, Lancaster, UK

    Cosmin Danut Bocaniala Phd

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Calado, J., Sá da Costa, J. (2006). Fuzzy Neural Networks Applied to Fault Diagnosis. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_10

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