基于IFM-VMD与WTD-Hilbert结合的滚动轴承故障诊断

Fault Diagnosis of Rolling Bearing Based on IFM-VMD and WTD-Hilbert

针对变模式分解(VMD)中分解层数K对分解结果准确度影响较大以及轴承振动信号夹杂的噪声极大地影响有用信息提取的问题,提出了一种利用瞬时频率均值(IFM)确定K值并结合小波阈值降噪(WTD)和Hilbert变换对轴承的振动信号进行分析的方法。首先利用瞬时频率均值选择合适的VMD中的K值,然后用VMD方法对含噪声的信号进行自适应分解,根据相关系数原则从分解的分量中选取含有主要故障信息的分量进行小波阈值降噪分析,最后进行Hilbert变换解调出故障特征频率。为验证此方法的可行性,首先通过仿真信号验证了所用降噪方法的可靠性,然后用提出的IFM-VMD与WTD-Hilbert结合的方法对实际轴承故障数据进行分析,该方法故障诊断的准确率达到99%以上,说明该方法可以很好地识别滚动轴承的故障信息。

For decomposition layer K of Variational Mode Decomposition(VMD)has a greater influence on the accuracy of the decomposition result and bearing vibration signal with noise greatly affects useful information extraction,a kind of using instantaneous frequency mean to determine the K and combining with the Wavelet Threshold De-noising(WTD)and Hilbert transform to analyze vibration signals of bearings was put forward.In this method,the instantaneous frequency mean value was used to select the appropriate K value in VMD and then the signal containing noise was decomposed adaptively by using VMD method.According to the principle of correlation coefficient,the components containing the main fault information were selected from the decomposed components for wavelet threshold noise reduction analysis and finally the fault characteristic frequency was demodulated by Hilbert transform.To verify the feasibility of the method,the noise reduction method reliability was verified by simulation signal,then the proposed IFM-VMD and WTD-Hilbert method was used to analyze the actual bearing failure data.The accuracy of the fault diagnosis method is more than 99%,so the method is a good way to identify rolling bearing fault information.