Abstract
This chapter focuses on the fault diagnosis of artefacts often met in industry, but not only, that execute various functions involving conductive flows of matter and energy, i.e., multifunctional conductive flow systems (MCFSs). The proposed MCFS abstraction is close to the human diagnostician way of conceiving entities and relations on physical, functional and behavioural structures. Diagnosis reasoning, performed by human diagnosticians, is intrinsically abductive reasoning. This chapter presents the abduction by plausibility and relevance in a connectionist approach. The case study on a hydraulic installation of a rolling mill plant gives examples on the knowledge elicitation process and on the diagnostic expert system building and running.
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Ariton, V. (2006). Soft Computing Models for Fault Diagnosis of Conductive Flow Systems. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_8
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DOI: https://doi.org/10.1007/978-1-84628-631-5_8
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