The Open Automation and Control Systems Journal

2015, 7 : 303-313
Published online 2015 April 17. DOI: 10.2174/1874444301507010303
Publisher ID: TOAUTOCJ-7-303

Faults Diagnosis of Railway Bearing Based on FIR-wavelet Packet and LVQ Neural Network

Yang Jianwei , Yao Dechen , Li Xi , Jia Limin and Qin Yong
School of Machine-electricity and Automobile Engineering, Beijing University of Civil Engineering Architecture, Beijing, 100044, P.R. China.

ABSTRACT

In this paper, we presented a way for railway bearing fault diagnosis with the use of FIR-wavelet packet and LVQ neural network. First, the original vibration signal of trains’ rolling bearing is denoised based on FIR. Then, the signals after de-noised are preprocessed by wavelet packet and the wavelet packet energy eigenvector is reconstructed. Those kinds of wavelet packet energy eigenvectors are used to train LVQ neural network. Finally, the intelligent fault diagnosis is realized. The result shows that this approach is effective to distinguish this kind of rolling bearing faults. This method has important practical value.

Keywords:

FIR, Wavelet packet, LVQ neural network, Railway rolling bearing, Fault diagnosis, De-noised.