基于改进SSA的参数优化VMD和ELM的轴承故障诊断  

Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA

作　　者：杨森 王恒迪[1] 崔永存[1] 李畅 唐元超 Yang Sen;Wang Hengdi;Cui Yongcun;Li Chang;Tang Yuanchao(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471003,China;SIPPR Engineering Group Co.,Ltd.,Zhengzhou 450007,China;Shandong Chaoyang Bearings Co.,Ltd.,Dezhou 253200,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471003 [2]机械工业第六设计研究院有限公司,河南郑州450007 [3]山东朝阳轴承有限公司,山东德州253200

出　　处：《机械传动》2023年第10期162-168,共7页Journal of Mechanical Transmission

基　　金：山东省重点研发计划(2020CXGC011003)

摘　　要：针对滚动轴承早期故障信号微弱、故障特征难以提取导致故障分类效果差的问题,提出了一种基于改进麻雀搜索算法(Sparrow Search Algorithm,SSA)进行自适应参数优化的变分模态分解(Variational Mode Decomposition,VMD)和多层特征向量融合的极限学习机(Extreme Learning Ma⁃chine,ELM)的滚动轴承故障诊断方法。首先,根据适应度函数值和迭代次数自适应改变SSA的寻优步长;随后,将改进后的SSA对VMD算法的重要参数(分解个数K和惩罚因子α)进行自动寻优,适用度函数采用最小包络谱熵;接着,提取经SSA-VMD分解后的包络谱熵最小的内蕴模态函数(In⁃trinsic Mode Function,IMF)分量作为最优分量,并计算其特征值;最后,通过变异系数法筛选,构造均方根值和峰值为第一层二维特征值向量,构造样本熵、峭度和均方根为第二层三维特征值向量,分别送入极限学习机ELM进行滚动轴承故障的训练分类。试验结果表明,本文算法具有良好的故障诊断效果且最终可实现98.25%的分类准确率和93.36%的实际诊断精度。Aiming at the problem that the initial fault signal of rolling bearings is weak and the fault characteristic is difficult to extract,this study proposes a rolling bearing fault diagnosis method based on variational modal decomposition(VMD)for adaptive parameter optimization based on the improved sparrow search algorithm(SSA)and the extreme learning machine(ELM)with multi-layer feature vector fusion.Firstly,the optimization step size of SSA is adaptively changed according to the fittness function value and the number of iterations.Secondly,the improved SSA optimizes the important parameters(decomposition number K and penalty factorα)of the VMD algorithm,and the fittness function adopts the minimum envelope entropy.Thirdly,the intrinsic mode function(IMF)component with the smallest envelope spectral entropy after SSA-VMD decomposition is extracted as the optimal component,and its eigenvalue is calculated.Finally,through the screening of coefficients of the variation method,the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer,and the sample entropy,kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer,which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25%and an actual diagnostic accuracy of 93.36%.

关 键 词：滚动轴承 早期故障诊断 变分模态分解 改进麻雀算法 变异系数法 极限学习机 

分 类 号：TH133.33[机械工程—机械制造及自动化]