基于MODWPT和MCKD的滚动轴承早期故障诊断

Early Fault Diagnosis of Rolling Bearing Based on MODWPT and MCKD

针对齿轮啮合强振动干扰下滚动轴承微弱故障特征提取难的问题,提出一种最大重叠离散小波包变换(MODWPT)和最大相关峭度解卷积(MCKD)相结合的滚动轴承早期故障诊断方法。首先采用MODWPT方法将复杂的轴承故障振动信号分解为若干分量,然后依据峭度准则,选取峭度较大的分量进行MCKD滤波,最后对滤波后所得信号做Hilbert包络分析,将包络谱呈现的频率特征与理论故障特征频率相比较,识别故障特征,实现故障诊断。通过轴承故障的仿真及实验研究,并对比单一MCKD方法和EMD-MED方法的提取效果,说明该方法可以在一定程度上抑制齿轮啮合强振动及噪声的干扰,增强并有效提取出滚动轴承早期低频微弱故障特征。

Aiming at the problem that it is difficult to extract the weak fault features of rolling bearings under the strong vibration interference of gear meshing,a rolling bearing early fault diagnosis method based on the combination of maximum overlapping discrete wavelet packet transform(MODWPT)and maximum correlation kurtosis deconvolution(MCKD)is proposed.Firstly,the complex bearing fault vibration signal is decomposed into several components using the MODWPT method,and then the components with larger kurtosis are selected for MCKD filtering according to the kurtosis criterion.Finally,the filtered signal is analyzed by Hilbert envelope analysis,and the frequency characteristics presented by the envelope spectrum are compared with the theoretical fault characteristic frequency to identify the fault characteristics and realize fault diagnosis.Through the simulation and experimental study of bearing fault,and comparing the extraction performance of the MCKD method and the EMD-MED method,it shows that this method can suppress the interference of strong vibration and noise in gear meshing to a certain extent,and enhance and effectively extract the early low-frequency weak fault features of rolling bearings.