The Bidirectional Associative Memory Neural Network based on Fault Tree and Its Application to Inverters Fault Diagnosis
With study on Fault Tree Analysis (FTA) and Bidirectional Associative Memory (BAM) neural network, a new method of intelligent fault diagnosis is proposed. All the knowledge on the happening of top events is stored in Fault tree, in which the whole fault modes are obtained. The priori knowledge and experience of system diagnosis are introduced to FTA. The learning sample of BAM neural network is deduced by the corresponding relations between the fault modes and the fault analysis. The diagnosis results are associated parallel by the associative memory matrix; also the general ability of fault diagnosis is being expanding. With experiments and application to inverters fault diagnosis, results show that this method has better performance for realtime and effectivity.
fault tree analysis neural network bidirectional associative memory fault diagnosis inverter
Bo Fa Yixin Yin Cunfa Fu
School of Information Engineering,University of Science and technology Bejing,Bejing,China;Electroni School of Information Engineering,University of Science and technology Bejing,Bejing,China CITIC Heavy Industries Co.Ltd,Luoyang,China
国际会议
上海
英文
209-213
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）