Machine Learning
Fuzzy neural classifier for transformer fault diagnosis based on EM learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Turbo-generator vibration fault diagnosis based on PSO-BP neural networks
WSEAS Transactions on Systems and Control
A Bayesian framework for parameters estimation in complex system
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
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In accordance with intelligent complementary strategies, a new transformer fault diagnosis method is proposed based on rough set (RS) and fuzzy set (FS) and Bayesian optimal classifier in this paper. Through RS reduction, the diagnostic decision table is greatly simplified and fault symptoms information is compressed, dramatically, and the minimal decision rules can be obtained. In the light of the minimal decision rules, the complexity of Bayesian reasoning and difficulties of fault symptom acquisition are dramatically decreased. Moreover, probability reasoning may be realized applying Bayesian optimal classifier, it can be used to describe the characteristics of fault information and investigate the fault reasons of transformer. In the end, a practical application in transformer fault diagnosis indicates that the proposed method is very effective and intelligent and ubiquitous.