Bearing Fault Diagnosis Based On Wavelet Packet - The Locomotive Of The Neural Network Research

Wheel rolling bearing is a very important part in railway locomotive but easily broken. In railway Major incidents caused by serious rolling bearing fault happens every now and then which brings on people's lives and property security risks, so research on locomotive rolling bearing fault diagnosis is an urgent subject. This paper summarized the work of other relevant issues, achieving rolling bearing fault diagnosis and classify of locomotive in-transit.This paper firstly researched the principle of rolling bearing noises and the abnormal sounds produced by uneven bearing cup, cone or roller or corrosion, etc. and their characteristics, analyzing the various feature parameters and spectral structure of the bearing acoustic signal in time domain and frequency domain. The best feature parameters was selected after the performance comparison for fault diagnosis, providing a theoretical basis for rolling bearing fault diagnosis by acoustic signal. Then, the paper introduced the basic principle of wavelet analysis and application, and the continuous wavelet transform, discrete wavelet transform, wavelet packet analysis and a variety of commonly used wavelet bases, and their respective applied occasions, decomposing and reconstructing the acoustic signal of normal bearings and single cup/cone/roller spall bearings, extracting the feature parameters as the input parameters of neural network. The power spectrum after envelope demodulation could be used as reference for the accuracy of pattern recognition. Finally, the paper used particle swarm optimized neural network into fault pattern recognition. Optimizing BP neural network by particle swarm optimization algorithm was proposed after researching the defects of BP neural network, comparing with the common BP neural network in pattern recognition with bearing acoustic signal feature parameters, training and testing using the sample data after wavelet packet analysis and processing.The paper analyzed the railway locomotive bearing fault diagnosis system based on wavelet packet and PSO optimized neural network. The experiment show that this method is effective and feasible and good to the in-transit railway truck fault early warning system.