On the Motor Fault Diagnosis Based on Wavelet Transform and ANN

摘要：

Digital signal processing methods are adopted to carry on the smart diagnosis on the electric motor fault type judgment on MATLAB platform. Firstly, we gathered electrical machinerys sound signals in different running conditions and de-noised these signals by using wavelet method in time domain and frequency domain. Next, the signal’s energy eigenvector is analyzed and extracted. Finally, the neural network sorter was operated to classify the quantified electric motor fault sound. Several methods are adopted throughout the whole process of de-noising, extraction of the energy eigenvector and neural network recognition. The comparison of these methods is also made so as to select the optimal one for the electric motor fault type diagnosis. The experiments indicated that the smart diagnosis introduced in this article achieved high rate of accuracy in the electric motor fault type recognition based on the noise analysis.

关键词： electric motor noise fault diagnosis wavelet packet analysis energy eigenvector artificial neural network (ANN)

作者: Wancheng WANG Qin HUANG Yuan ZHANG

作者单位: College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 5339-5346

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）