Optimizing VMD and Improving Residual Network Research on Gearbox Fault Diagnosis

郭梓良; 郝如江; 杨文哲; 王一帆; 张建超

doi:10.13433/j.cnki.1003-8728.20230130

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Optimizing VMD and Improving Residual Network Research on Gearbox Fault Diagnosis

更新时间：2026-07-10

- Optimizing VMD and Improving Residual Network Research on Gearbox Fault Diagnosis
- Mechanical Science and Technology for Aerospace Engineering Vol. 43, Issue 12, Pages: 2132-2137(2024)
- 作者机构：
  
  石家庄铁道大学机构机械工程学院,石家庄,050043
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230130
  CLC：
- Published：2024
- 稿件说明：
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郭梓良, 郝如江, 杨文哲, et al. Optimizing VMD and Improving Residual Network Research on Gearbox Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(12): 2132-2137.
DOI：

郭梓良, 郝如江, 杨文哲, et al. Optimizing VMD and Improving Residual Network Research on Gearbox Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(12): 2132-2137. DOI： 10.13433/j.cnki.1003-8728.20230130.

摘要

为提高齿轮箱故障诊断的准确率，采用变分模态分解（Variational mode decomposition

VMD）和一维残差网络（Resnet）相结合的齿轮箱故障识别方法。首先使用磷虾群算法对VMD中的惩罚因子α \mathit{\mathit{\alpha}} 和分解层数K K 进行寻优，其次使用VMD对故障信号进行分解产生若干条本征模函数（Intrinsic mode function

IMF）并通过使用相关系数对IMF分量进行筛选，并将筛选后的分量作为改进残差网络的输入数据进行网络训练和测试，得到故障识别结果。通过与VMD-随即森林、VMD-CNN等方法的平均准确率进行横向比较，结果表明，本文的方法用来识别故障类型更为准确。

Abstract

In order to improve the accuracy of gearbox fault diagnosis

this paper adopts a gearbox fault identification method combining VMD (Variational mode decomposition) and 1D-Resnet (One-dimensional residual network). Firstly

the krill swarm algorithm is used to optimize the penalty factor α and the number of decomposition layers K in the VMD. Secondly

the VMD is used to decompose the fault signal to generate several IMF (Intrinsic mode functions)

and the correlation coefficient is used to screen the IMF component. Finally

the screened component is used as the input data of the improved residual network for network training and testing

and the fault identification result is obtained. Through the horizontal comparison with the average accuracy of VMD-random forest

VMD-CNN and other methods

the results show that the method in this paper is more accurate for identifying gearboxfault types.

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