Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning

Zhenguo Jie; Xiyang Wang; Tingkai Gong

doi:10.16578/j.issn.1004.2539.2022.05.024

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Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning

Development·Application | 更新时间：2022-09-24

- Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning
- Journal of Mechanical Transmission Vol. 46, Issue 5, Pages: 160-166(2022)
- 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
  3.故障诊断与健康管理技术航空科技重点实验室，上海 201601
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.05.024
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揭震国,王细洋,龚廷恺.基于分布适配层和软标签学习的齿轮故障诊断[J].机械传动,2022,46(05):160-166.

Jie Zhenguo,Wang Xiyang,Gong Tingkai.Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning[J].Journal of Mechanical Transmission,2022,46(05):160-166.
揭震国,王细洋,龚廷恺.基于分布适配层和软标签学习的齿轮故障诊断[J].机械传动,2022,46(05):160-166. DOI： 10.16578/j.issn.1004.2539.2022.05.024.

Jie Zhenguo,Wang Xiyang,Gong Tingkai.Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning[J].Journal of Mechanical Transmission,2022,46(05):160-166. DOI： 10.16578/j.issn.1004.2539.2022.05.024.

摘要

基于卷积神经网络的齿轮智能识别算法能有效地识别齿轮故障，但卷积神经网络需要大量的已标注训练数据，制约了卷积神经网络在齿轮故障诊断上的应用。针对该问题，提出了基于分布适配层和软标签学习的齿轮故障诊断方法。采用卷积神经网络提取特征和软标签；通过分布适配层提取分布差异，软标签学习生成软标签损失；以分布差异、软标签损失与分类损失生成的联合损失为目标函数，训练模型并进行目标域故障诊断。采用齿轮振动信号验证了提出方法，结果表明，提出方法能准确有效地分类齿轮故障数据。

Abstract

The intelligent gear recognition method based on convolutional neural network can effectively identify the gear fault， but the convolutional neural network needs a lot of labeled training data， which limits the application of convolutional neural network in gear fault diagnosis. To solve this problem， a gear fault diagnosis method based on distribution adaptation layer and soft label learning is proposed. The convolutional neural network is used to extract features and soft labels. The distribution discrepancy is extracted by the distribution adaptation layer， and the soft label loss is generated by the soft label learning. The joint loss of distribution discrepancy， soft label loss and classification loss are used as the objective function， and the model is trained to diagnose the faults of target domain. The proposed method is verified by gear vibration signals. The results show that the proposed method can classify gear fault data accurately and effectively.

关键词

齿轮故障诊断卷积神经网络深度迁移学习分布适配层软标签学习

Keywords

Gear fault diagnosisConvolutional neural networkDeep transfer learningDistribution adaptation layerSoft label learning

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