Motor Fault Diagnosis based on Wavelet Energy and Immune Neural Network

摘要：

Motor fault diagnosis methods are crucial in acquiring safe and reliable operation in motor drive systems. In this paper, a new method for the motor fault diagnosis is proposed based on wavelet packet transform (WPT) and artificial neural network (ANN). The energy of the vibration signals of motor can be obtained by the multidecomposition of WPT and used as feature values of ANN inputs for fault diagnosis system. The artificial immune algorithm (AIA) for data clustering is employed to adaptively choose the centers and widths of the hidden layer centers of the radial basis function neural network (RBFNN). The simulation experiment results show the applicability and effectiveness of the proposed method to motor fault diagnosis.

关键词： motor fault diagnosis wavelet energy artificial immune system RBF neural network

作者: Xin Wen David Brown Honghai Liu Qizheng Liao Shimin Wei

作者单位: Institute of Industrial Research University of Portsmouth Portsmouth,POI 3QL,UK School of Automation Beijing University of Posts and Telecommunications Beijing,100876,China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 1592-1596

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）