基于深度迁移学习的齿轮故障诊断方法

刘世豪; 王细洋; 龚廷恺

doi:10.16578/j.issn.1004.2539.2023.05.021

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基于深度迁移学习的齿轮故障诊断方法

开发应用 | 更新时间：2023-05-31

- 基于深度迁移学习的齿轮故障诊断方法
- Gear Fault Diagnosis Method Based on Deep Transfer Learning
- 机械传动 2023年47卷第5期页码：134-142
- 作者机构：
  
  1.南昌航空大学飞行器工程学院，江西南昌 330063
  2.南昌航空大学通航学院，江西南昌 330063
- 作者简介：
  
  刘世豪（1999— ），男，江西南昌人，硕士研究生；研究方向为故障诊断；523112232@qq.com。
  王细洋（1967— ），男，江西湖口人，教授，硕士研究生导师；研究方向为故障诊断、先进制造等；838796648@qq.com。
- 基金信息：
  
  国家自然科学基金项目(51465040┫南昌航空大学研究生创新专项资金项目┣YC2021-059);国家自然科学基金(51875174)
- DOI：10.16578/j.issn.1004.2539.2023.05.021
  中图分类号：
- 纸质出版日期：2023-05-15，
  
  收稿日期：2022-03-18，
  
  修回日期：2022-04-26，
扫描看全文
刘世豪,王细洋,龚廷恺.基于深度迁移学习的齿轮故障诊断方法[J].机械传动,2023,47(05):134-142.

Liu Shihao,Wang Xiyang,Gong Tingkai.Gear Fault Diagnosis Method Based on Deep Transfer Learning[J].Journal of Mechanical Transmission,2023,47(05):134-142.
刘世豪,王细洋,龚廷恺.基于深度迁移学习的齿轮故障诊断方法[J].机械传动,2023,47(05):134-142. DOI： 10.16578/j.issn.1004.2539.2023.05.021.

Liu Shihao,Wang Xiyang,Gong Tingkai.Gear Fault Diagnosis Method Based on Deep Transfer Learning[J].Journal of Mechanical Transmission,2023,47(05):134-142. DOI： 10.16578/j.issn.1004.2539.2023.05.021.

摘要

针对齿轮故障样本欠缺问题，提出一种基于Hilbert-Huang谱和预训练VGG16模型的迁移学习故障诊断方法。对振动信号进行经验模态分解（Empirical Mode Decomposition，EMD）得到本征模态函数（Intrinsic Mode Function，IMF），同时取相关系数最大的IMF做Hilbert变换，获取时频谱；利用预训练VGG16提取变负载下和各健康状态下齿轮的Hilbert-Huang谱图像特征；采用全局均值池化层取代VGG16模型部分全连接层，进行分类输出。实验结果表明，在少量的样本数据下，该方法的齿轮故障诊断准确率达到98.86%，优于TLCNN和Tran VGG-19等迁移学习方法，证明了该方法在齿轮故障诊断中具有一定研究价值。

Abstract

Aiming at the problem of insufficient gear fault samples

a fault diagnosis method of transfer learning based on Hilbert-Huang spectrum and pre-trained VGG16 model is proposed. Firstly

the intrinsic mode function (IMF) is obtained by Empirical Mode Decomposition (EMD) of vibration signals

and the time spectrum is obtained by Hilbert transform of IMF with the largest correlation coefficient. Secondly

pre-trained VGG16 is used to extract Hilbert-Huang spectrum image features of gears under varying loads and under various health conditions. Finally

the global average pooling layer is used to replace partial full connection layer of VGG16 model for classification output. Experimental results show that with a small amount of sample data

the accuracy of gear fault diagnosis reaches 98.86%

which is better than the transfer learning methods such as TLCNN and Tran VGG-19. It is proved that the method presented in this paper has some research value in gear fault diagnosis.

关键词

迁移学习VGG16模型Hilbert-Huang谱齿轮故障诊断全局均值池化

Keywords

Transfer learningVGG16Hilbert-Huang spectrumGear fault diagnosisGlobal average pooling

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