Non-linear minimum variance estimation for fault detection systems

Alkan Alkaya, Michael John Grimble

Research output: Contribution to journalArticle

2 Citations (Scopus)
78 Downloads (Pure)

Abstract

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully.
Original languageEnglish
Pages (from-to)805-812
Number of pages8
JournalTransactions of the Institute of Measurement and Control
Volume37
Issue number6
Early online date24 Sep 2014
DOIs
Publication statusPublished - 29 May 2015

Keywords

  • condition monitoring
  • estimation
  • fault detection/diagnosis
  • non-linear observer
  • non-linear systems

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