Online fault diagnosis of nonlinear systems based on neurofuzzy networks

H. T. Mok, C. W. Chan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


Artificial intelligence techniques such as neural networks and fuzzy logic have been widely used in fault detection and diagnosis. Combining these two techniques, referred to as neurofuzzy networks, provides a powerful tool for modelling. B-spline neurofuzzy networks are used to model the residuals. The weights of the networks are trained online using recursive least squares method. Fuzzy rules are extracted from the networks and they provide linguistic description of the residuals. The qualitative information of the residuals facilitates isolation of the system faults. The proposed scheme is illustrated using a simulation example of a DC motor.

Original languageEnglish
Title of host publicationProceedings of the 16th IFAC World Congress, IFAC 2005
PublisherIFAC Secretariat
Number of pages6
ISBN (Print)008045108X, 9780080451084
Publication statusPublished - 2005

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


  • Fault diagnosis
  • Fuzzy logic
  • Motor
  • Neural networks
  • Nonlinear systems


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