基于迁移学习的零样本故障诊断

吴天舒; 尹宏鹏; 赵丹丹; 蔡力

doi:10.12263/DZXB.20211681

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基于迁移学习的零样本故障诊断

学术论文 | 更新时间：2025-12-08

- 基于迁移学习的零样本故障诊断
- Zero Sample Fault Diagnosis Based on Transfer Learning
- 电子学报 2023年51卷第9期页码：2572-2577
- 作者机构：
  
  重庆大学，重庆 404100
- 作者简介：
  
  [ "吴天舒男，1989年6月出生于重庆市.2020年毕业于重庆大学计算机科学专业，获博士学位.现在Cloudwork担任博士后研究员.主要研究方向为数据挖掘、分布式计算、工业视觉等. E-mail: tswu@cqu.edu.cn" ]
  [ "尹宏鹏（通讯作者）男，1981年8月出生于江西省莲花市.2009年毕业于重庆大学控制科学与控制工程.现为重庆大学自动化学院教授，从事模式处理、工业视觉等研究." ]
  [ "赵丹丹女，1997年10月出生于云南省曲靖市.现为重庆大学自动化学院控制科学与工程博士.主要研究方向为图像智能处理与分析、故障诊断、迁移学习等. E-mail: whsmhgy@gmail.com" ]
  [ "蔡力男，1996年出生于四川省.现为重庆大学自动化学院在读博士生，主要研究方向为寿命预测、故障诊断等.E-mail: c201506060101@163.com" ]
- 基金信息：
  
  重庆英才计划青年拔尖人才项目(cstc2021ycjh-bgzxm0028);重庆英才计划项目(cstc2021ycjh-bgzxm0028);国家自然科学基金面上项目(1010200720170220)
- DOI：10.12263/DZXB.20211681
  中图分类号： TP183
- 收稿：2021-12-23，
  
  修回：2022-05-06，
  
  纸质出版：2023-09-25
- 稿件说明：
移动端阅览
吴天舒,尹宏鹏,赵丹丹等.基于迁移学习的零样本故障诊断[J].电子学报,2023,51(09):2572-2577.

WU Tian-shu,YIN Hong-peng,ZHAO Dan-dan,et al.Zero Sample Fault Diagnosis Based on Transfer Learning[J].ACTA ELECTRONICA SINICA,2023,51(09):2572-2577.
吴天舒,尹宏鹏,赵丹丹等.基于迁移学习的零样本故障诊断[J].电子学报,2023,51(09):2572-2577. DOI： 10.12263/DZXB.20211681.

WU Tian-shu,YIN Hong-peng,ZHAO Dan-dan,et al.Zero Sample Fault Diagnosis Based on Transfer Learning[J].ACTA ELECTRONICA SINICA,2023,51(09):2572-2577. DOI： 10.12263/DZXB.20211681.

摘要

针对工业故障诊断中设备故障数据采集困难，目标故障样本少的问题，现有的零样本故障诊断方法仍依赖于故障数据集，本文提出了一种基于迁移学习的零样本故障诊断方法.通过经典的主成分分析算法，构建了一个适用于源域和目标域的判别属性提取器，用于提取源域数据样本潜在的细粒度特征表示，将其作为知识迁移的桥梁.利用源域故障数据获得所有已知故障类的共享细粒度基组，并将其作为知识迁移到目标域故障表示中.从共享细粒度基组学习源域和目标域的判别矩阵，构建各自的判别性特征，最终利用判别性属性实现零样本的故障诊断.基于田纳西-伊斯曼过程（Tennessee Eastrman Process，TEP）数据集，实验对本文方法和其他零样本故障诊断方法进行对比，实验结果验证了本文方法对零样本故障检测的有效性.

Abstract

For the problem of difficulty in collecting equipment fault data and the shortage of target fault samples in industrial fault diagnosis

existing zero sample fault diagnosis method still depends on the fault datasets

a zero sample fault diagnosis method based on transfer learning is proposed. Through the classic PCA (Principal Components Analysis) algorithm

a discriminant attribute extractor applied to source domain and target domain is constructed

to extract the potential fine-grained feature representation of source domain data samples as a bridge for knowledge transfer. The shared fine-grained base group of all known fault classes is obtained from the source domain fault data and transferred as knowledge to the target domain fault representation. The discriminant matrices of source domain and target domain are learned from the shared fine-grained basis group

thus the discriminant characteristics of each domain are constructed. Finally

the discriminant attributes are used to realize zero sample fault diagnosis. Based on the tennessee-eastrman process (TEP) datasets

the proposed method is compared with other zero sample fault diagnosis methods

and the experimental results illustrates the effectiveness of our method for zero sample fault diagnosis.

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references

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