基于双级对齐部分迁移网络的旋转设备故障诊断

俞昆; 程玉虎; 邢镔; 王雪松

doi:10.12263/DZXB.20221132

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基于双级对齐部分迁移网络的旋转设备故障诊断

学术论文 | 更新时间：2025-12-11

- 基于双级对齐部分迁移网络的旋转设备故障诊断
- Fault Diagnosis of Rotating Equipment Based on Double-Level Aligned Partial Transfer Network
- 电子学报 2023年51卷第12期页码：3529-3539
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院,江苏徐州 221116
  2.重庆工业大数据创新中心有限公司工业大数据应用技术国家工程实验室,重庆400707
- 作者简介：
  
  [ "俞昆男，1991年生，山东济宁人.现为中国矿业大学信息与控制工程学院讲师、硕士生导师.主要研究方向为机电设备智能运维、振动信号时频分析." ]
  [ "程玉虎男，1973年生，安徽淮南人.现为中国矿业大学信息与控制工程学院教授、博士生导师.主要研究方向为机器学习、迁移学习和智能系统. E-mail: chengyuhu@163.com" ]
  [ "邢镔男，1962年生，法国籍.现为工业大数据应用技术国家工程实验室首席科学家，工业大数据国家级博士后科研工作站负责人.主要研究方向为工业大数据、数字孪生. E-mail: xing.bin@hotmail.com" ]
  [ "王雪松（通讯作者）女，1974年生，安徽泗县人.现为中国矿业大学信息与控制工程学院教授、博士生导师.主要研究方向为机器学习、人工智能. E-mail: wangxuesongcumt@163.com" ]
- 基金信息：
  
  国家自然科学基金(62176259;62206298);江苏省自然科学基金(BK20221111);中国博士后科学基金资助项目(2022M710542);中央高校基本科研业务费专项资金资助(2022QN1053)
- DOI：10.12263/DZXB.20221132
  中图分类号： TP181
- 收稿：2022-10-10，
  
  修回：2022-12-19，
  
  纸质出版：2023-12-25
- 稿件说明：
移动端阅览
俞昆,程玉虎,邢镔等.基于双级对齐部分迁移网络的旋转设备故障诊断[J].电子学报,2023,51(12):3529-3539.

YU Kun,CHENG Yu-hu,XING Bin,et al.Fault Diagnosis of Rotating Equipment Based on Double-Level Aligned Partial Transfer Network[J].ACTA ELECTRONICA SINICA,2023,51(12):3529-3539.
俞昆,程玉虎,邢镔等.基于双级对齐部分迁移网络的旋转设备故障诊断[J].电子学报,2023,51(12):3529-3539. DOI： 10.12263/DZXB.20221132.

YU Kun,CHENG Yu-hu,XING Bin,et al.Fault Diagnosis of Rotating Equipment Based on Double-Level Aligned Partial Transfer Network[J].ACTA ELECTRONICA SINICA,2023,51(12):3529-3539. DOI： 10.12263/DZXB.20221132.

摘要

随着智能制造和工业大数据的快速发展，迁移学习在旋转设备故障诊断领域得到了广泛研究.在工业现场，存在大量目标域标签空间为源域标签空间子集的场景，现有迁移学习方法在处理此类场景时，无法消除源域离群类别对目标域分类产生的负迁移影响.部分迁移学习通过限制源域不同类别数据在特征对齐过程的贡献度，实现源域和目标域共享类别特征对齐.然而，现有部分迁移学习方法仅考虑源域和目标域共享类别边缘分布对齐，未考虑源域和目标域共享类别各子类间的状态分布对齐，诊断正确率仍有待提高.为此，本文以Vision Transformer网络为基础网络架构，提出基于双级对齐部分迁移网络的故障诊断方法：一方面构造加权平衡机制促进源域和目标域共享类别间的边缘分布对齐，另一方面利用度量学习实现源域和目标域共享类别各子类间的状态分布对齐.利用滚动轴承故障数据对所提方法进行验证，结果表明：所提方法在所有诊断案例中的准确率均在95%以上，相比其他对比方法表现出更优的诊断效果.

Abstract

With the rapid development of intelligent manufacturing and industrial big data

transfer learning has been widely applied in the field of fault diagnosis of rotating equipment. In the industrial field

there are a large number of scenarios where the label space of the target domain is a subset of the label space of the source domain. The existing transfer learning is unavailable to eliminate the effect of negative transfer of the outlier categories of the source domain on the classification performance of the target domain. In contract

partial transfer learning can realize the feature alignment between the shared categories of the source domain and target domain data by limiting the contribution of source data of different categories in the feature alignment process. However

the existing partial transfer learning only considers the marginal distribution alignment between the shared categories of the source domain and target domain data

while neglecting the conditional distribution alignment between the shared categories of the source domain and target domain data

which leads to a poor accuracy of the diagnostic task. To address this issue

a partial transfer fault diagnosis method based on double-level aligned partial transfer network is proposed. In the proposed method

vision transformer is adopted as the basic network to extract the global feature information from both source domain and target domain data. Meanwhile

a weighting balance mechanism is constructed to promote the marginal distribution alignment between the shared categories of the source domain and target domain data

and a series of metric learning measures are used to realize the conditional distribution alignment between the shared categories of the source domain and target domain data. The identification accuracies of the proposed method in all bearing cases are more than 95%

which verifies the superiority of the proposed method in comparison with other diagnostic methods.

关键词

Keywords

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