Deep and Shallow Knowledge in Fault Diagnosis

  • Viorel Ariton
Conference paper

DOI: 10.1007/978-3-540-45224-9_101

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)
Cite this paper as:
Ariton V. (2003) Deep and Shallow Knowledge in Fault Diagnosis. In: Palade V., Howlett R.J., Jain L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science, vol 2773. Springer, Berlin, Heidelberg

Abstract

Diagnostic reasoning is fundamentally different from reasoning used in modelling or control: last is deductive (from causes to effects) while first is abductive (from effects to causes). Fault diagnosis in real complex systems is difficult due to multiple effects-to-causes relations and to various running contexts. In deterministic approaches deep knowledge is used to find ”explanations” for effects in the target system (impractical when modelling burden appear), in softcomputing approaches shallow knowledge from experiments is used to links effects to causes (unrealistic for running real installations). The paper proposes a way to combine shallow knowledge and deep knowledge on conductive flow systems at faults, and offers a general approach for diagnostic problem solving.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Viorel Ariton
    • 1
  1. 1.”Danubius” University from GalatiGalatiRomania

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