基于SSA-VMD-MCKD的强背景噪声环境下滚动轴承故障诊断  

Fault diagnosis of rolling bearing under strong background noise based on SSA-VMD-MCKD

作　　者：任良 甄龙信[1] 赵云[1] 董前程 张云鹏[1] REN Liang;ZHEN Longxin;ZHAO Yun;DONG Qiancheng;ZHANG Yunpeng(Hebei Key Laboratory of Special Carrier Equipment,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]燕山大学河北省特种运载装备重点实验室,河北秦皇岛066004

出　　处：《振动与冲击》2023年第3期217-226,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金(51675462)。

摘　　要：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(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。Here, to effectively extract weak fault features of rolling bearing and accurately diagnose faults under strong background noise, a rolling bearing fault diagnosis method combining singular spectral analysis(SSA), variational mode decomposition(VMD) and maximum correlated kurtosis deconvolution(MCKD) was proposed. Firstly, SSA algorithm was used to decompose fault signal, and decomposed signals were screened and reconstructed according to the time domain cross-correlation criterion. Secondly, the whale optimization algorithm(WOA) was used to optimize parameters alpha, K of VMD and L and M of MCKD, respectively. The VMD with optimized parameters was used to decompose the reconstructed signal, and fault feature signals were extracted from intrinsic mode functions(IMFs) obtained with decomposition according to the kurtosis index. Thirdly, the MCKD with optimized parameters was used to enhance fault characteristics. Finally, fault diagnosis was performed using spectrum envelope. Simulation and tests showed that the proposed method can effectively extract and diagnose bearing faults under strong noise interference.

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 

分 类 号：TH133.33[机械工程—机械制造及自动化]