Zero Sample Fault Diagnosis Based on Transfer Learning

WU Tian-shu; YIN Hong-peng; ZHAO Dan-dan; CAI Li

doi:10.12263/DZXB.20211681

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Zero Sample Fault Diagnosis Based on Transfer Learning

PAPERS | 更新时间：2025-12-08

- Zero Sample Fault Diagnosis Based on Transfer Learning
- ACTA ELECTRONICA SINICA Vol. 51, Issue 9, Pages: 2572-2577(2023)
- 作者机构：
  
  重庆大学，重庆 404100
- 作者简介：
- 基金信息：
  
  Chongqing Talent Plan Youth Top Talent Project(cstc2021ycjh-bgzxm0028);Chongqing Talent Plan Project(cstc2021ycjh-bgzxm0028);National Natural Science Foundation of China(1010200720170220)
- DOI：10.12263/DZXB.20211681
  CLC： TP183
- Received：23 December 2021，
  
  Revised：2022-05-06，
  
  Published：25 September 2023
- 稿件说明：
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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.

关键词

Keywords

references

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