三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用

发布时间：2018-03-17 07:31

本文选题：轴承故障诊断　切入点：经验小波变换　出处：《电子测量与仪器学报》2017年08期 　论文类型：期刊论文

【摘要】：实际应用中研究机械系统的工作状态时,通常会对其所产的信号进行研究分析,从而得出相关结论。这些由机械系统产生的信号一般含有多种不同波动的混合成分,为了得出可靠的结论,必须从复合信号和背景噪声中分离出有物理意义的成分。因此引入一种新的故障提取方法,首先利用一种较新的模态分解算法——经验小波变换,将一组信号分解成多个具有紧支撑傅里叶频谱的调幅-调频(AM-FM)分量;然后利用K-L散度值挑选出具有物理意义的分量;最后将挑选出的分量通过三点对称差分能量算子运算,得到其能量谱的同时也能得到瞬时频率,从而提取出故障特征。将该方法用于模拟信号和实际轴承故障信号,并且同之前的方法进行对比。结论表明,该方法不仅能很好的提取轴承故障特征,而且证明该方法具有更好的优越性。
[Abstract]:When studying the working state of a mechanical system in practical application, the signals produced by the mechanical system are usually studied and analyzed, and the relevant conclusions are drawn. These signals produced by the mechanical system generally contain a variety of mixed components with different fluctuations. In order to get a reliable conclusion, the physical components must be separated from the composite signal and background noise. Therefore, a new fault extraction method is introduced. Firstly, a new mode decomposition algorithm, empirical wavelet transform, is used. A set of signals is decomposed into several amplitude-frequency modulation (AM FM) components with compact support Fourier spectrum, and then the physical components are selected by using K-L divergence value. Finally, the selected components are calculated by a three point symmetric differential energy operator. At the same time, the instantaneous frequency can be obtained, and the fault characteristics can be extracted. The method is used to simulate the signal and the actual bearing fault signal, and compared with the previous method. The conclusion shows that, This method can not only extract the bearing fault features well, but also prove that this method has better advantages.
【作者单位】：长安大学道路施工技术与装备教育部重点实验室;
【基金】：中央高校教育教学改革专项经费建设项目(jgy16049,0012-310600161000)资助
【分类号】：TH133.3

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