基于WOA-VMD联合MOMEDA的轴承外圈故障特征提取方法  

Fault feature extraction method of bearing outer ring based on WOA-VMD combined with MOMEDA

作　　者：王莹莹 陈志刚 王衍学 WANG Yingying;CHEN Zhigang;WANG Yanxue(School of Mechanical Electronic and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Construction Safety Monitoring Engineering Technology Research Center,Beijing 100044,China)

机构地区：[1]北京建筑大学机电与车辆工程学院,北京100044 [2]北京市建筑安全监测工程技术研究中心,北京100044

出　　处：《机电工程》2023年第11期1655-1663,共9页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51875032);北京市属高校基本科研业务费专项(X20061);北京市建筑安全监测工程技术研究中心研究基金资助课题(BJC2020K011);北京建筑大学硕士研究生创新项目(PG2023128)。

摘　　要：滚动轴承工作环境较为复杂,在复杂的环境因素影响下,其故障特征信号容易受到噪声的影响,导致其难以被识别。针对这一问题,提出了一种基于鲸鱼优化算法(WOA)的变分模态分解(VMD)联合多点最优最小熵解卷积(MOMEDA)的滚动轴承外圈故障特征提取方法。首先,利用变分模态分解(VMD)对仿真信号进行了分解,使用鲸鱼优化算法(WOA)确定了最佳分解层数以及各分量的样本熵;然后,以样本熵最小值为目标寻优,得出了包含故障信号的最佳分量,对得到的最佳分量进行了MOMEDA重构,从重构信号的包络谱中获得了仿真信号故障特征频率及其倍频;最后,为了验证WOA-VMD联合MOMEDA的有效性,在实验台上采集数据,对滚动轴承的外圈故障信号进行了特征提取。实验结果表明:使用该方法可以高效地进行信号的分解寻优,能较为准确地得到仿真信号的故障频率(100 Hz)和实验台提取信号的近似故障频率(87.5 Hz),验证了该方法的有效性。研究结果表明:低信噪比的工况条件下,采用WOA-VMD联合MOMEDA的方法可以有效地提取滚动轴承的故障特征信号,并能从重构信号中提取故障特征频率。In order to solve the problem that the rolling bearing working environment was more complex and the fault characteristic signal was easily affected by noise and difficult to be discriminated,a fault feature extraction method of bearing outer ring based on whale optimization algorithm(WOA)of variational modal decomposition(VMD)combined with multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)was proposed.First of all,the simulated signal was decomposed using VMD,and the optimal number of decomposition layers and the sample entropy of each component was determined using WOA.Then the optimal component containing the fault signal was obtained with the minimum value of sample entropy as the target search.The best component obtained was reconstructed by MOMEDA,and the fault characteristic frequency of the simulated signal and its multiplier frequency were obtained from the envelope spectrum of the reconstructed signal.Finally,In order to verify the effectiveness of WOA-VMD combined with MOMEDA,data were collected on the experimental platform,and the characteristics of outer ring fault signals of rolling bearings were extracted.The experimental results show that the method can be used to efficiently decompose the signal seeking,and can more accurately obtain the fault frequency of 100 Hz of the simulated signal and the approximate fault frequency of 87.5 Hz of the extracted signal of the experimental bench using the method,which verifies the effectiveness of the method.The research results show that WOA-VMD combined with MOMEDA can effectively extract the fault characteristic signal of rolling bearing under the condition of low signal-to-noise ratio and extract the fault characteristic frequency from the reconstructed signal.

关 键 词：故障信号分解 故障信号重构 鲸鱼优化算法 变分模态分解 样本熵 多点最优最小熵解卷积 故障特征频率 

分 类 号：TH133.33[机械工程—机械制造及自动化]