Abstract
A computational system based on application of artificial intelligence techniques to perform on-line fault detection and diagnosis is presented. The diagnosis is based on a fuzzy qualitative simulation algorithm. The adoption of fuzzy sets allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space and also allows common-sense knowledge to be represented in defining values through the use of graded membership. Through machine learning techniques, the system can adjust the fuzzy membership function of the process variables automatically, as well as build the knowledge base on-line very efficiently.
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