An intelligent online fault diagnostic scheme for nonlinear systems

Hing Tung Mok, Che Wai Chan, Zaiyue Yang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions.

Original languageEnglish
Pages (from-to)267-272
Number of pages6
JournalJournal of Control Theory and Applications
Volume6
Issue number3
DOIs
Publication statusPublished - Aug 2008

Keywords

  • Fault diagnosis
  • Neurofuzzy networks
  • Nonlinear systems

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