为在强背景噪声环境下有效提取滚动轴承微弱故障特征并准确诊断故障，提出奇异谱分析（Singular Spectrum Analysis，SSA）、变分模态分解（Variational Mode Decomposition，VMD）和最大相关峭度解卷积（Maximum Correlated Kurtosis Deconvolution，MCKD）结合的滚动轴承故障诊断方法。首先，利用SSA算法将故障信号分解，根据时域互相关准则对分解信号筛选重构；其次，利用鲸鱼优化算法（The Whale Optimization Algorithm，WOA）分别优化VMD的参数alpha，K以及MCKD的参数L和M，利用参数优化的VMD对重构信号进行分解，根据峭度指标从分解所得的本征模态函数（Intrinsic Mode Function，IMF）中提取故障特征信号；再次，利用参数优化的MCKD算法增强故障特征；最后，通过频谱包络进行故障诊断。仿真和试验表明所提方法能在强噪声干扰下有效提取并诊断轴承故障。
Abstract
In order to effectively extract the weak fault characteristics of rolling bearing and accurately diagnose the fault in the environment of strong background noise, a rolling bearing fault diagnosis method combining singular spectrum analysis (SSA), variational mode decomposition (VMD) and maximum correlated kurtosis deconvolution (MCKD) was proposed. Firstly, the fault signal was decomposed by SSA algorithm, and the decomposed signal was filtered and reconstructed according to the time-domain cross-correlation criterion; Secondly, the whale optimization algorithm (WOA) was used to optimize the parameters alpha, K of VMD and L and M of MCKD respectively. The reconstructed signal was decomposed by the parameter optimized VMD, and the fault characteristic signal was extracted from the decomposed intrinsic mode function (IMF) according to the kurtosis index; Thirdly, the parameter optimized MCKD algorithm was used to enhance the impact characteristics in the fault characteristic signal; Finally, fault diagnosis was carried out through spectrum envelope. Simulation and experiments show that the proposed method can effectively extract and diagnose bearing faults under the interference of strong background noise.
关键词
奇异谱分析（SSA） /
变分模态分解（VMD） /
最大相关峭度解卷积（MCKD） /
鲸鱼仿生优化算法（WOA） /
轴承故障诊断
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Key words
singular spectrum analysis (SSA) /
variational modal decomposition (VMD) /
maximum correlation kurtosis deconvolution (MCKD) /
whale bionic optimization algorithm (WOA) /
bearing fault diagnosis
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