Rolling Bearing Fault Diagnosis Based On Acoustic Emission Technique Railway Overloaded Trucks

Heavy transport has been internationally recognized as the direction of the development of rail freight, along with the rapid development of heavy haul trucks in the safety and reliability of a higher demand. And the trucks key parts of the Rolling precisely with the safety and reliability of the trucks are closely related. Rolling in the high-speed heavy run if there is a fault, the fault will rapidly expanding, likely to cause heat-axis, fuel-axis, cut-axis so that finally led to the train subversion and other major traffic accident occurred in a short period of time, resulting in significant economic losses. Therefore, bearing the operational status of the real-time monitoring and fault diagnosis, preventive measures is particularly important. In this paper, acoustic emission technique to detect the rolling bearing fault signal pattern recognition, signal processing and analysis using wavelet packet analysis method using neural network fault information.In this paper, at home and abroad on the rolling bearing fault detection method, combined with the rolling bearing fault types and causes, Rolling diagnostic method based on acoustic emission technology. Introduced a system of acoustic emission signal processing method, comparative analysis, wavelet transform is more suitable for extracting rolling bearing fault. Details of the basic principles and algorithms of wavelet transform, describes the characteristics of commonly used wavelet bases, how to select wavelet basis of acoustic emission signals of rolling bearing. The characteristics of rolling bearings of acoustic emission signals using wavelet packet analysis method to extract the rolling bearing fault information, the processed feature vector input to the BP neural network rolling bearing fault pattern recognition, and then determine the type of bearing failure and fault. After use a lot of the actual Rolling experimental data to be verified, and the results show that the effectiveness of the use of this method. In this paper,there are38figures,5tables and76references.