Neural networks-based fault detection and isolation of nonlinear systems

  • I. S. Lee
  • , J. T. Kim
  • , J. W. Lee
  • , Y. J. Lee
  • , K. Y. Kim

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

5 Scopus citations

Abstract

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
EditorsO. Coastillo
Pages142-147
Number of pages6
StatePublished - 2003
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Cancun, Mexico
Duration: 19 May 200321 May 2003

Publication series

NameProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence

Conference

ConferenceProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
Country/TerritoryMexico
CityCancun
Period19/05/0321/05/03

Keywords

  • Fault detection
  • Isolation
  • Neural network
  • Nonlinear system
  • Statistical method

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