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

Yang Sen; Wang Hengdi; Cui Yongcun; Li Chang; Tang Yuanchao

doi:10.16578/j.issn.1004.2539.2023.10.023

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

Development·Application | 更新时间：2023-10-09

- Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA
- Journal of Mechanical Transmission Vol. 47, Issue 10, Pages: 162-168(2023)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.10.023
  CLC：
- Published：15 October 2023，
  
  Received：21 July 2022，
  
  Revised：11 August 2022，
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杨森,王恒迪,崔永存等.基于改进SSA的参数优化VMD和ELM的轴承故障诊断[J].机械传动,2023,47(10):162-168.

Yang Sen,Wang Hengdi,Cui Yongcun,et al.Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA[J].Journal of Mechanical Transmission,2023,47(10):162-168.
杨森,王恒迪,崔永存等.基于改进SSA的参数优化VMD和ELM的轴承故障诊断[J].机械传动,2023,47(10):162-168. DOI： 10.16578/j.issn.1004.2539.2023.10.023.

Yang Sen,Wang Hengdi,Cui Yongcun,et al.Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA[J].Journal of Mechanical Transmission,2023,47(10):162-168. DOI： 10.16578/j.issn.1004.2539.2023.10.023.

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 Machine，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

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2.53999996

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和惩罚因子

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）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（Intrinsic 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。

Abstract

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

and penalty factor

) 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

Keywords

Rolling bearingEarly fault diagnosisVariational mode decompositionImproved sparrow search algorithmCoefficient of variation methodExtreme learning machine

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