Deep and Shallow Knowledge in Fault Diagnosis

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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.