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Deep and Shallow Knowledge in Fault Diagnosis

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2773))

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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Authors and Affiliations

  1. ”Danubius” University from Galati, Lunca Siretului no.3, 6200, Galati, Romania

    Viorel Ariton

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  1. Viorel Ariton
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Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, United Kingdom

    Vasile Palade

  2. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, BN2 4GJ, Brighton, United Kingdom

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, 5095, Adelaide, SA, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/978-3-540-45224-9_101

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  • DOI: https://doi.org/10.1007/978-3-540-45224-9_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40803-1

  • Online ISBN: 978-3-540-45224-9

  • eBook Packages: Springer Book Archive

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