Rolling Bearing Fault Diagnosis Based on Wavelet Energy Spectrum, PCA and PNN

摘要：

　　In order to solve the problem that the excessive dimensions of feature vector will lead to probabilistic neural network(PNN)s structure becoming complicated and recognition rate slowing down when we take the wavelet energy spectrum of the rolling bearing vibration signal as the feature vector,a novel approach based on wavelet energy spectrum,principal component analysis(PCA)and probabilistic neural network(PNN)is proposed.The method firstly decomposes the vibration signal by wavelet transform algorithm,separately reconstructs the wavelet coefficients of each level,and calculates each frequency bands signal energy in the time domain as the feature vector.Then,we use the principal component analysis(PCA)technology to process wavelet energy spectrum so as to reduce its dimensions.Lastly,we feed the principal components into the PNN for recognition.The experimental results show that the proposed method not only can accurately recognize the test set,but also can reduce the dimensions of input feature vector in order to simplify network model,reduce the time required for recognition,and improve the recognition efficiency.

关键词： Rolling bearing Fault diagnosis Wavelet energy spectrum Probabilistic neural network Principal component analysis

作者: Keyong Shao Miaomiao Cai Guofeng Zhao

作者单位: College of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China The First Oil Testing Team,The Third Oil Extraction Plant,Daqing 163256,China

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: 800-804

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）