Supervision, fault-detection and fault-diagnosis methods — An introduction

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Abstract

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper gives an introduction to the field of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure that is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators are another source of information, and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then-rules.

References (45)

  • D. Barschdorff et al.

    Neuronale Netze als Signal- und Musterklassifikatioren

    Technisches Messen

    (1990)
  • R.V. Beard

    Failure accomodation in linear systems through self-reorganization

  • J.P. Burg

    A new analysis technique for time series data

  • R.N. Clark

    A simplified instrument detection scheme

    IEEE Trans. Aerospace Electron. Syst.

    (1978)
  • R.N. Clark

    Instrument fault detection

    IEEE Trans. Aerospace Electron. Syst.

    (1978)
  • P.M. Frank et al.

    Robust fault diagnosis using unknown input observer schemes

  • B. Freyermuth

    Knowledge based incipient fault diagnosis of industrial robots

  • B. Freyermuth

    Wissensbasierte Fehlerdiagnose am Beispiel eines Industrieroboters

  • R.A. Frost

    Introduction to knowledge base systems

    (1986)
  • J. Gertler

    Survey of model-based failure detection and isolation in complex plants

    IEEE Control Systems Magazine

    (1988)
  • S. Halgamuge

    Advanced Methods for Fusion of Fuzzy System and Neural Networks in Intelligent Data Processing

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