A Decision Model for Transformer Fault Diagnosis and Maintenance Based on Rough Set and Fuzzy Set and Bayesian Optimal Classifier
Hongsheng Su · 2006
Based on Bayesian optimal classifier, combining with rough set and fuzzy set, a new transformer fault diagnosis and maintenance mode is presented in the paper. In this method fuzzy membership function of the observed information are used to establish posterior probability of primary assumptions in Bayesian optimal classifier, the classified result based on each fault information is for that worked out, the optimal diagnosis result is acquired after all these results are weighted average. Then based on rough model of Bayesian risk decision, the diagnosis results of all fault information are identified to constitute possible maintenance strategies. Actual application shows that the proposed method can deal with the "bottle neck" of fuzzy knowledge acquisition in Bayesian optimal classifier and possesses stronger learning abilities, and is an effective transformer fault diagnosis and maintenance method.