Study On Fault Diagnosis System Of The Bearing Of Rail Transit Vehicles

With the development of rail transport in our country, rail vehicles safety or not is closely related to passengers'safety. In recent years, the complexity of rail vehicles equipment is increasing, and the failure rate is rising. Therefore, how to diagnose the bearing of rail transit vehicles efficiently, rapidly and accurately is an important issue to be resolved.In this paper, based on the rigorous summarization to resent rail transit fault diagnosis technology in the world, we have conducted and analyzed the fault occurrence of the Bearing, and identified the rules and characteristics of fault knowledge. Based on the above needs analysis and research, wavelet transform, Hilbert transform, Neural Network was used in this paper, Firstly, rolling bearing signal is denoised. Then, three-layer wavelet packet is adopted to decompose the signal and reconstruct energy eigenvector. Last, 1) Hilbert transform, 2) fault samples of wavelet packet energy eigenvectors are used as neural network input parameters to realize intelligent fault diagnosis. Examples with real data demonstrate:(1) The experimental result proves that the fault characteristic extracted from improved wavelet packet and Hilbert transform is in accord with the one analyzed from theory, and the fault feature extration method is effective.(2) The practice example shows that the trained BP, Elman, RBF can diagnose this kind of rolling bearing faults, the method has fair prospects of application for the rotary machine fault diagnosis.(3) The generalization capability of RBF is superior to that of BP. Meanwhile, In the training time, RBF is also superior to that of BP networkThe characteristics of the bearing and extraction methods was presented, the advantages and disadvantages of some methods was brief introduced, then the system's overall architecture was proposed, and each part is described in detail.Finally, this paper has developed the fault diagnosis system of the bearing of rail transit vehicles. First, functional modules are described, and then system operational interface is presented. In this paper, the fault frequency was found by Hilbert transform; The wavelet and neural network was used to diagnose the kind of fault of the bearing, it realizes intelligent fault diagnosis, and then ensures the safety of rail transit vehicles, fast operation, this system has good practicability.This has important practical value.