基于参数自适应的RSSD-CYCBD及在轴承外圈故障特征提取中的应用  

RSSD-CYCBD based on parameter adaptation and its application in feature extraction of bearing outer ring faults

作　　者：刘晖 姚德臣 杨建伟[1,2] 魏明辉 LIU Hui;YAO Dechen;YANG Jianwei;WEI Minghui(School of Machine-electricity and Automobile Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles,Beijing University of Civil Engineering and Architecture,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]北京建筑大学城市轨道交通车辆服役性能保障北京重点实验室,北京100044

出　　处：《机电工程》2024年第5期836-844,共9页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51975038,52272385,52205083);北京市自然科学基金资助项目(L211008,L221027,L211007,3214042)。

摘　　要：针对滚动轴承工作环境复杂、故障特征信号易被高强度噪声掩盖的问题,提出了基于参数自适应的共振稀疏分解(RSSD)和最大二阶循环平稳盲解卷积(CYCBD)的滚动轴承故障诊断方法。首先,利用人工大猩猩部队优化算法(GTO),结合相关系数与相关峭度的融合指标,自适应选择RSSD分解参数,得到了仿真信号的最优低共振分量;然后,利用GTO结合包络熵,自适应选择CYCBD的循环频率和滤波器长度,对最优低共振分量进行了解卷积运算,从包络谱中获得了信号的故障特征频率;最后,利用美国凯斯西储大学试验台和MFS-MG机械故障综合模拟试验台数据,综合验证了该方法的有效性,并将试验结果与RSSD-MCKD方法的结果进行了对比。研究结果表明,该方法能够准确地得到仿真信号的故障频率为20 Hz、美国凯斯西储大学试验台近似故障频率为107.5 Hz、MFS-MG试验台近似故障频率为87.6 Hz。自适应RSSD-CYCBD方法能够有效地识别出故障特征频率及其倍频,实现滚动轴承故障诊断的目的。In order to solve the problem that the working environment of rolling bearings is complex and the fault characteristic signals are easily masked by high-intensity noise.An improved fault diagnosis method for rolling bearings based on the combination of resonance sparse decomposition(RSSD)and maximum second-order cyclic stationary blind deconvolution(CYCBD)was proposed.Firstly,the artificial gorilla troops optimization(GTO)algorithm was used to adaptively select the RSSD decomposition parameters by combining the fusion index of correlation coefficient and correlation kurtosis,and the optimal low resonance component of the simulation signal was obtained.Then,GTO combined with envelope entropy was used to adaptively select the cycle frequency and filter length of CYCBD,and the optimal low resonance component was deconvoluted,and the fault eigenfrequency of the signal was obtained from the envelope spectrum of the low resonance component.Finally,the effectiveness of the method was verified by using the data of Case Western Reserve University test bench and MFS-MG mechanical fault comprehensive simulation test bench,and the test results were compared with the RSSD-MCKD method.The results show that the proposed method can accurately obtain the fault frequency of the simulated signal of 20 Hz,the approximate fault frequency of the Case Western Reserve University test bench of 107.5 Hz,and the approximate fault frequency of the MFS-MG test bench of 87.6 Hz,and the effect of fault feature extraction is better than that of RSSD-MCKD.The RSSD-CYCBD method can effectively identify the fault eigenfrequency and its frequency doubling,and realize fault diagnosis.

关 键 词：滚动轴承 故障诊断 共振稀疏分解 最大二阶循环平稳盲反卷积 人工大猩猩部队优化算法 包络熵 高强度噪声 

分 类 号：TH133.33[机械工程—机械制造及自动化]