Fault Diagnosis of Subway Auxiliary Inverter Based on EEMD and GABP

摘要：

　　Focusing on the non-stationary characteristic of the fault signal of subway auxiliary inverter,this paper proposes the method that combines ensemble empirical mode decomposition(EEMD)with genetic algorithm to optimize BP neural network(GABP)to diagnose the fault categories of subway auxiliary inverter.Firstly,this paper extracts feature vectors from the original fault signal by EEMD,then establishes the multi-fault diagnosis model by GABP.The genetic algorithm(GA)is introduced to search the optimal solutions of initial weight and thresholds of BP neural network(BPNN),so as to improve the convergence and precision of diagnosis of network.Simulation results show that this method we proposed can identify these faults more accurately and higher efficiently.

关键词： EEMD GA BPNN Fault diagnosis

作者: Liang Cheng Junwei Gao Bin Zhang Ziwen Leng Yong Qin

作者单位: College of Automation Engineering,Qingdao University,Qingdao 266071,China College of Automation Engineering,Qingdao University,Qingdao 266071,China;State Key Laboratory of Ra State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,C

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 4715-4719

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）