Lei Gao , Qinhe Gao , Zhihao Liu , Hongjie Cheng , Jianyong Yao , Xiaoli Zhao , Sixiang Jia
{"title":"Multiple classifiers inconsistency-based deep adversarial domain generalization method for cross-condition fault diagnosis in rotating systems","authors":"Lei Gao , Qinhe Gao , Zhihao Liu , Hongjie Cheng , Jianyong Yao , Xiaoli Zhao , Sixiang Jia","doi":"10.1016/j.ress.2025.111017","DOIUrl":null,"url":null,"abstract":"<div><div>Unknown fault operating conditions and the absence of fault data pose significant challenges for real-time fault diagnosis, as the generalization capability of models is heavily reliant on transferable knowledge from a single operating condition. To overcome these limitations, a novel deep adversarial domain generalization framework based on multiple classifiers inconsistency (DADG-MCI) is designed to improve generalized ability without the need for target domain data during training. Initially, unique features of the multiple source domains are captured through the probability output inconsistency of the multiple domain-specific classifiers. Subsequently, adversarial training facilitates finer-grained global feature alignment across multiple source domains, which ensures that the extracted deep features possess strong generalization capabilities. Most importantly, DADG-MCI introduces the multiple classifiers inconsistency to measure multi-domain distributional discrepancy based on Wasserstein distance, which captures feature distribution differences between domains through joint optimization of the multi-classifier module. Finally, two challenging rotating machinery fault datasets are used to evaluate the performance of DADG-MCI for cross-condition fault diagnosis. Compared to several state-of-the-art methods, DADG-MCI achieves the highest average diagnostic accuracies and successfully applies to unseen operating conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111017"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832025002182","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Unknown fault operating conditions and the absence of fault data pose significant challenges for real-time fault diagnosis, as the generalization capability of models is heavily reliant on transferable knowledge from a single operating condition. To overcome these limitations, a novel deep adversarial domain generalization framework based on multiple classifiers inconsistency (DADG-MCI) is designed to improve generalized ability without the need for target domain data during training. Initially, unique features of the multiple source domains are captured through the probability output inconsistency of the multiple domain-specific classifiers. Subsequently, adversarial training facilitates finer-grained global feature alignment across multiple source domains, which ensures that the extracted deep features possess strong generalization capabilities. Most importantly, DADG-MCI introduces the multiple classifiers inconsistency to measure multi-domain distributional discrepancy based on Wasserstein distance, which captures feature distribution differences between domains through joint optimization of the multi-classifier module. Finally, two challenging rotating machinery fault datasets are used to evaluate the performance of DADG-MCI for cross-condition fault diagnosis. Compared to several state-of-the-art methods, DADG-MCI achieves the highest average diagnostic accuracies and successfully applies to unseen operating conditions.
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.