Rolling Bearing Fault Diagnosis Of Go Line Department In Railway Train

ABSTRACT:As urban rail transit of China develops rapidly, the operation safety problem of railway highlights increasingly. Urban rail transit train is complex operation system integration of electromechanical integration. The evolving process of the running gear system state and performance will form the security hidden danger, and even cause accidents, which influences seriously people daily travel and causes serious social problem. Fault diagnosis based on neural network is an important research direction to intelligent fault diagnosis of the theory and technology. This paper is to fault diagnosis of running gear rolling bearing on the following research1. Aiming at the problems of rolling bearing fault diagnosis of running gear in railway line, based on wavelet packet analysis, the BP neural network and evidence theory, this paper puts forward a comprehensive fault diagnosis method. In for acceleration signal wavelet packet decomposition premise, trained by use of neural network, the goal of fault recognition achieves. And through the fusion of training success probability of the evidence theory to different neural network, the best fault diagnosis method is chosed relatively.2. Through acceleration signal of four kind of rolling bearing with fault as the inner loop, the outer loop, the rolling element and the normal rolling bearing, earring on the wavelet packet analysis, using analysis of the original data of three, four wavelet packet, taking the results as the input sample of the BP neural net for network training, the paper choose the right wavelet packet to decompos layers.3. Training by using samples of different neural network to wavelet packet decomposition production, with the help of fault diagnosis and recognition for railway train running gear of the rolling bearing, the bearing fault position is found out. This paper links judiciously wavelet packet theory with algorithm, draws on their advantages, improves the efficiency of the bearing fault diagnosis.4. Based on evidence theory method, each fault position diagnosis probability further fuse to select the optimal diagnostic method. Considered evidence for different fault type the diagnostic accuracy, realizing fusion in the time domain and the space domain, comparing the results of fusion, to select the optimal diagnostic methods, the accuracy of the diagnosis improves.