基于优化MCKD-VMD与互相关谱的轴承复合故障诊断

Compound Fault Diagnosis of Bearings Based on Optimized MCKD-VMD and Cross-correlation Spectrum

针对强背景噪声下轴承复合故障诊断困难的问题,提出一种优化最大相关峭度解卷积(MCKD)和变分模态分解(VMD)并结合互相关谱的轴承复合故障诊断方法。首先,通过黏菌优化算法(SMA)优化MCKD的参数,实现故障信号解卷积及故障特征增强,完成复合故障特征分离;其次,对分离的解卷积信号进行互相关谱分析,对故障特征不明显的解卷积信号使用VMD进行降噪;最后,对降噪的解卷积信号进行互相关谱分析,进一步降低噪声的影响,提取故障特征完成故障诊断。仿真和实验结果表明,所提方法克服了仅使用MCKD或包络谱的缺点,能够有效降低噪声的影响,复合故障诊断效果较好。

Aiming at the difficulty of compound fault diagnosis of bearings under strong background noise,a compound fault diagnosis method of bearings was proposed,which optimized maximum correlation kurtosis deconvolution(MCKD)and variational mode decomposition(VMD)and combined with cross-correlation spectrum.Firstly,the parameters of MCKD were optimized by slime mould algorithm(SMA),and the fault signal deconvolution and fault feature enhancement were realized,and the compound fault feature separation was completed.Secondly,cross-correlation spectrum analysis was performed on the separated deconvolution signals,and VMD was used to reduce the noise of the deconvolution signals with no obvious fault characteristics.Finally,cross-correlation spectrum analysis is carried out on de-noised deconvolution signals to further reduce the influence of noise and extract fault features to complete fault diagnosis.Simulation and experimental results show that the proposed method overcomes the shortcoming of using only MCKD or envelope spectrum,can effectively reduce the influence of noise,and has good compound fault diagnosis effect.