Fault Diagnosis of Railway Axlebox Bearing based on Wavelet Packet and Neural Network

摘要：

　　A real time and effective axlebox bearing fault diagnostic method is significant in the condition-based maintenance.In the axlebox bearing fault diagnostic system, fault features extraction and fault patterns classification are two important aspects to identify whether a axlebox bearing is failure or not.This paper presents a method of axlebox bearing fault diagnosis based on wavelet packet decomposition and BP neural network.First decompose the vibration signal into a finite number of coefficients by wavelet packet decomposition.Then calculate energy moment of each coefficient and take the energy moment as an eigenvector to effectively express the failure feature.Finally BP neural network is used for fault classification.The experimental results show that combining wavelet packet decomposition with BP neural network could identify the axlebox bearing fault effectively.The average diagnosis accuracy rate is 96.67%.

关键词： Wavelet packet Energy moment Neutral network axlebox bearing Fault diagnosis

作者: Xiaofeng Li Yunxiao Fu Limin Jia

作者单位: School of Traffic and Transportation,Beijing Jiaotong University,Beijing,China School of Electrical Engineering,Beijing Jiaotong University,Beijing,China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing,China

会议类型: 国际会议

会议名称: the 2012 International Conference on Vibration, Structural Engineering and Measurement (2012年振动、结构工程与测量国际会议(ICVSEM2012))

会议地点: 上海

会议语种:英文

页码: 749-755

在线出版日期: 2012-10-19（万方平台首次上网日期，不代表论文的发表时间）