Fault semantic knowledge transfer learning: Cross-domain compound fault diagnosis method under limited single fault samples

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Huaitao Xia , Tao Meng , Zonglin Zuo , Wenjie Ma
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引用次数: 0

Abstract

The coupling of faults leads to an exponential growth of compound fault types, making it impractical to collect complete labeled compound fault data in real-world scenarios. While cross-domain compound fault diagnosis (the target-domain does not have labeled compound fault data) is crucial for system reliability, existing methods often rely on abundant single-fault samples and rarely validate the reliability when single-fault data is limited. To overcome this limitation, we propose a novel fault semantic knowledge transfer learning framework. Specifically, FSKTL incorporates inter-class semantic distance loss in the source-domain, enabling fault classification through low-dimensional fault semantics and identifying the optimal fault semantic correlation function. Subsequently, FSKTL introduces inter-domain semantic alignment loss in the target-domain. This approach not only preserves the semantic space optimized by the source-domain for fault classification, but also achieves domain adaptation, enhancing the cross-domain generalization of the optimal fault semantic correlation function. Finally, extensive experiments are conducted on two publicly available datasets to validate the effectiveness of the proposed method. The results demonstrate that compared to other methods, this approach achieves the highest accuracy in cross-domain compound and single fault diagnosis.
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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