{"title":"在不同条件和故障模式下预测剩余使用寿命的对比域不变广义方法","authors":"Xiaoqi Xiao , Jianguo Zhang , Dan Xu","doi":"10.1016/j.ress.2024.110534","DOIUrl":null,"url":null,"abstract":"<div><div>As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110534"},"PeriodicalIF":9.4000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes\",\"authors\":\"Xiaoqi Xiao , Jianguo Zhang , Dan Xu\",\"doi\":\"10.1016/j.ress.2024.110534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.</div></div>\",\"PeriodicalId\":54500,\"journal\":{\"name\":\"Reliability Engineering & System Safety\",\"volume\":\"253 \",\"pages\":\"Article 110534\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-10-05\",\"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/S0951832024006069\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832024006069","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}
Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes
As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.
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.