Steam Turbine Fault Diagnosis Method Based on Rough Set Theory and Bayesian Network
Pu Han, Deli Zhang, Lihui Zhou, Songming Jiao · 2007
Aiming at the uncertain problem in steam turbine fault diagnosis, a new method based on rough set theory and Bayesian network is proposed. Simplify expert knowledge and reduce fault symptoms using reduction approach of rough set theory, the minimal diagnostic rules can be obtained. According to the minimal rules, complexity of Bayesian network structure and difficulties of fault symptom acquisition are largely decreased. At the same time, probability reasoning can be realized by Bayesian network. Finally, the correctness and effectiveness of this method are validated by the result of practical fault diagnosis examples.