Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis

Chang Liu; Yanxue Wang; Jianwei Yang

doi:10.16578/j.issn.1004.2539.2020.05.024

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Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis

Development·Application | 更新时间：2022-11-10

- Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis
- Journal of Mechanical Transmission Vol. 44, Issue 5, Pages: 146-154(2020)
- 作者机构：
  
  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服役性能保障北京市重点实验室，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2020.05.024
  CLC：
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刘畅,王衍学,杨建伟.基于FOA的变分模态分解在轴承故障诊断中的应用[J].机械传动,2020,44(05):146-154.

Liu Chang,Wang Yanxue,Yang Jianwei.Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis[J].Journal of Mechanical Transmission,2020,44(05):146-154.
刘畅,王衍学,杨建伟.基于FOA的变分模态分解在轴承故障诊断中的应用[J].机械传动,2020,44(05):146-154. DOI： 10.16578/j.issn.1004.2539.2020.05.024.

Liu Chang,Wang Yanxue,Yang Jianwei.Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis[J].Journal of Mechanical Transmission,2020,44(05):146-154. DOI： 10.16578/j.issn.1004.2539.2020.05.024.

摘要

变分模态分解（VMD）广泛应用于故障诊断中，从振动信号中提取故障特征是故障诊断过程中的关键部分。针对强背景噪声和脉冲干扰下滚动轴承早期故障特征难以提取的问题，提出了一种新的基于果蝇优化算法（FOA）的变分模态分解的轴承故障诊断方法。首先，利用果蝇优化算法自适应优化VMD的惩罚参数, $α$ ,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34084931&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34084923&type=,2.20133328,2.62466669,和分解数,K,，获取最优参数组合；然后，对信号进行VMD分解，得到,K,个模态分量；最后，基于峭度最大化准则选取最优模态分量进行包络解调分析，提取出故障特征频率。通过仿真信号分析、实际故障轴承信号验证以及与基于果蝇优化算法的多分辨奇异值分解（MRSVD）方法进行对比，证明了所提方法的有效性。

Abstract

Abstract The variational mode decomposition （VMD） is widely used in fault diagnosis. Extracting fault characteristics from vibration signals is a critical part in the bearing fault diagnosis. It is difficult to extract early fault signatures under strong background noise and pulse interference. Thus， a new fault diagnosis method is proposed based on the variational mode decomposition and fruit fly optimization algorithm （FOA）. Firstly， the fruit fly optimization algorithm is used to optimize the penalty parameter α and the decomposition number K of the VMD adaptively to obtain the optimal parameter combination. Then， the VMD decomposition of the signal is performed to find K modal components. Finally， the optimal modal component is selected based on the kurtosis maximization criterion for envelope demodulation analysis and extracting the frequency of the fault feature. The effectiveness of the presented method is verified by simulation signal analysis， detecting bearing fault signal and comparing with multi-resolution singular value decomposition （MRSVD） approach based on fruit fly optimization algorithm.

关键词

变分模态分解果蝇优化算法多分辨奇异值分解轴承故障诊断

Keywords

Variational mode decompositionFruit fly optimization algorithmMulti-resolution singular value decompositionBearingFault diagnosis

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