Research on Turbo-generator Fault Diagnosis Using Probabilistic Neural Networks
ZHANG Jian-hua, HOU Guo-lian, SUN Xiao-gang, YUAN Gui-li(Department of Automation, North China University of Electric Power, Beijing 102206,China)
Based on probabilistic neural networks (PNN), a new way of fault diagnosis of steam turbine units is suggested to overcome problems met with back propagation neural networks (BPNN) like slow convergence of learning and liability of dropping into local minima. PNN can meet the needs of real-time requirements due to its simple learning algorithm, and quick training and generalizing property. In addition, newly trained patterns can easily be supplemented to the already trained classifier, thus facilitating the improvement of the accuracy of diagnosis results. Simulation results show that the proposed method is featured by swiftness, accuracy and ease of practical application.Fig 1,tables 2 and refs 9.