【作者】 姚德臣；

【导师】 殷玉枫； 杨建伟；

【作者基本信息】 太原科技大学 ， 车辆工程， 2010， 硕士

【摘要】 随着我国轨道交通运输的快速发展,机车作为承载乘客的直接载体,其安全与否直接与乘客的生命安全息息相关。近年来轨道交通车辆设备的复杂程度不断提高,故障率相应上升,而轴承作为轨道交通车辆最重要的部件之一,如何对其进行高效、快速而准确地诊断是值得研究的一个重要问题。本文在认真总结现阶段世界轨道交通故障诊断技术的基础上,总结分析了轴承故障发生情况,并找出故障发生的规律和特性。基于上述的需求分析,本文采用了小波、希尔伯特,神经网络等作为故障诊断方法对轨道交通车辆轴承进行了诊断,首先对采集到的信号进行降噪处理,再对降噪后的信号进行小波变换,对小波变换后的信号分别进行了:1)希尔伯特变换;2)提取特征向量,并将其输入到神经网络进行故障诊断。实验结果表明:(1)采用小波包和Hilbert包络谱分析,能有效地识别滚动轴承的故障特征,说明该故障特征提取方法是行之有效的;(2)采用的小波包和BP、Elman、RBF神经网络进行的滚动轴承故障诊断,能有效地识别故障特征,此方法同样适用于轴承等旋转机械的故障诊断;(3)无论从时间、还是输出准确程度上来看,RBF神经网络都明显优于BP神经网络。最后,对轨道交通车辆轴承故障诊断系统进行了软件实现。首先对各功能模块进行了说明,然后展示了系统的运行界面。应用希尔伯特变换找出了故障频率;结合小波与神经网络诊断出了故障类型,实现了对机车轴承的智能诊断,保证了机车的安全、快速运行,此系统具有较好的实用性。更多还原

【Abstract】 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.更多还原