The Fault Recognition of Motor Based on the Fusion of Neural Network and D-S Evidence Theory

摘要：

In order to reflect the motor from various aspects and realize the motor system state failure mode automatic identification and accurate diagnosis, neural network combined with the D-S evidence theory to form the motor fault diagnosis system. In data fusion level, fault characteristic is classified; and then the fault feature is extracted by the BP neural network and the local fault of the motor is diagnosed, as a result, the independent evidence is obtained; at last the D-S evidence theory fusion algorithm is used on the evidence to achieve the fault of the motor accurate diagnosis.Broken test proved that the diagnosis system improves the motor of the fault diagnosis of accuracy, and can meet the needs of real-time diagnosis. The diagnostic test proved that the diagnosis system improves the accuracy of motor fault diagnosis, and can satisfy the diagnosis in real-time.

关键词： fusion D-S evidence theory fault identificaiton neural network motor

作者: Hai-lian Du Zhan-feng Wang Feng Lv Tao Xin

作者单位: Electronic department, HeBei Normal University, Shijiazhuang, 050024,China Shijiazhuang University of Economics, Shijiazhuang,050031,China

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Applied Mechanics(2011年机电一体化与应用力学国际会议 ICMAM 2011)

会议地点: 香港

会议语种:英文

页码: 861-864

在线出版日期: 2011-12-27（万方平台首次上网日期，不代表论文的发表时间）