EIRP Proceedings, Vol 2 (2007)
COMBINED DEEP AND SHALLOW KNOWLEDGE IN A UNIFIED MODEL FOR DIAGNOSIS BY ABDUCTION
Abstract
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causeseffects)
but also deep knowledge (as structural / functional modularization and models on behavior). The paper
proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple
applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive
flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives
hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge
(according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic
installation in a rolling mill plant.
but also deep knowledge (as structural / functional modularization and models on behavior). The paper
proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple
applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive
flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives
hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge
(according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic
installation in a rolling mill plant.
References
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