Steam Turbine Fault Diagnosis Methods Based on the Main Constituent Analysis Method and Bayesian Network
Songming Jiao · Journal of Engineering for Thermal Energy and Power · 2008
When Bayesian network is used to diagnose a fault,the establishment of a model for fault diagnosis of steam turbines has a direct bearing on the complexity of the fault diagnosis process.Therefore,to establish a model of Bayesian network becomes an issue of first priority and the collection of characteristic parameters reflecting the fault status constitutes an important link for setting up a model.Through a discussion of the collection of fault characteristics by using the main constituent analysis method,presented was the modeling method for steam turbine fault diagnosis based on the main constituent analysis and Bayesian network.In addition,the proposed method has been compared with the traditional frequency characteristics modeling method.The results show that the model in question for turbine fault diagnosis is simple and lends itself to easy reasoning,thus enhancing the efficiency of turbine fault diagnosis.