基于SCSSA-VMD-MCKD的轴承早期微弱故障异常检测方法  

Early weak fault anomaly detection of bearing based on SCSSA-VMD-MCKD

作　　者：陈立海 谭奥 贺永辉 张笑琼 白晓龙 CHEN Lihai;TAN Ao;HE Yonghui;ZHANG Xiaoqiong;BAI Xiaolong(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471003,China;Collaborative Innovation Center of Hennan Province for High-End Bearing,Luoyang 471003,China;Postdoctoral Station,AECC Harbin Bearing Co.,Ltd.,Harbin 150500,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471003 [2]高端轴承河南省协同创新中心,河南洛阳471003 [3]中国航发哈尔滨轴承有限公司博士后科研工作站,黑龙江哈尔滨150500

出　　处：《机电工程》2024年第12期2129-2141,共13页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51805151);河南省博士后科研项目(202002065)。

摘　　要：针对滚动轴承在强噪声干扰下早期微弱故障不易被检测的问题,提出了一种基于结合正余弦和柯西变异的麻雀智能搜索算法优化变分模态分解与最大相关峭度解卷积(SCSSA-VMD-MCKD)的轴承早期微弱故障异常检测方法。首先,采用结合正余弦和柯西变异的麻雀智能搜索算法(SCSSA)优化了VMD参数α和K,进而对轴承故障信号进行了自适应分解,根据加权包络谱峰值因子指标(WEPF)筛选有效模态分量,并重构得到了重构信号;然后,采用SCSSA优化了MCKD参数T、L和M,并用优化后的MCKD方法增强了重构信号故障冲击成分;最后,对经MCKD增强后的重构信号进行了包络谱分析,提取到了轴承故障特征频率及倍频;利用轴承故障仿真信号和试验信号对该故障异常检测方法进行了验证分析。研究结果表明:该检测方法能够有效降噪并自适应增强故障冲击成分,相较于经SCSSA-VMD分解并重构的信号,故障仿真信号和实测试验信号信噪比分别提升了102.6%和81.3%,均方根误差分别降低了26.7%和33.3%;轴承内外圈故障特征频率及倍频幅值更为突出,能够实现强噪声背景下滚动轴承早期微弱故障异常检测目的,与SSA-VMD-MCKD方法相比,更能突显该方法的优越性。Aiming at the problem that weak early fault of rolling bearing is not easy to be detected under strong noise interference.A rolling bearing early weak fault anomaly detection method based on combining variational mode decomposition and maximum correlation kurtosis deconvolution optimized by sparrow search algorithm integrating sine-cosine and Cauchy mutation(SCSSA-VMD-MCKD)was proposed.Firstly,the VMD parameters includingαand K were optimized by sparrow search algorithm integrating sine-cosine and Cauchy mutation(SCSSA),the rolling bearing fault signal was adaptively decomposed,and the effective modal components screened by weighted envelope spectrum peak factor(WEPF)indexes were reconstructed to obtain the reconstructed signal.Then,the MCKD parameters including T、L and M were optimized by the SCSSA,and the reconstructed signal was processed by the MCKD in order to enhance the fault shock component.Finally,the envelope spectrum of the MCKD-enhanced signal was analyzed to extract the characteristic frequency and frequency doublings of rolling bearing faults.The fault anomaly detection method was verified and analyzed by bearing fault simulation signal and test signal.The research result show that comparing with the signal decomposed and reconstructed by SCSSA-VMD,the method can effectively reduce noise and adaptively enhance the fault impact component,the signal-to-noise ratio of fault simulation signal and actual test signal is respectively increased by 102.6%and 81.3%,and the root-mean-square error is respectively reduced by 26.7%and 33.3%.The amplitudes of bearing fault characteristic frequency and frequency doublings of inner and outer ring are more prominent,and the method can realize the early weak fault anomaly detection of rolling bearings under strong noise background.Comparing with the SSA-VMD-MCKD method,the superiority of the method is better emphasized.

关 键 词：滚动轴承 故障诊断 故障冲击成分增强 结合正余弦和柯西变异的麻雀智能搜索算法 变分模态分解 最大相关峭度解卷积 

分 类 号：TH133.33[机械工程—机械制造及自动化]