Abstract
This paper describes a method of extracting diagnostic rules from trained diagnostic feedforward neural nets that are constructed to recognise different mechanical faults using automated weight and structure learning algorithms. The rule extracting method is based on an interpretation that considers hidden neurons as partitions in the input space. An initial set of rules is then generated from the training data and the subspaces defined by the partitions. A procedure consisting of a number of algorithms is then used to simplify and reduce the set of initial rules step by step. To demonstrate and evaluate the rule extraction method, diagnostic rules for detecting a high-pressure air compressor's (HPAC) suction and discharge valve faults were extracted from static measurements including temperatures and pressures of various stages of the compressor.
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