{"title":"小故障样本下的跨域在线诊断框架","authors":"Zuoshuang Chen;Dongdong Zhang;Zuoyi Chen;Jun Wu;Hong-Zhong Huang","doi":"10.1109/TIM.2025.3606021","DOIUrl":null,"url":null,"abstract":"Cross-domain fault diagnosis in industrial applications presents a major challenge, especially when only a limited number of fault samples are available. Existing data-driven and transfer learning (TL) methods often struggle with real-time diagnosis, insufficient generalization across domains, and the inability to adapt to continuously evolving fault conditions. To address these limitations, this article proposes a novel cross-domain online fault diagnosis framework (CDODF). The framework leverages the contrastive language-image pretraining (CLIP) model to extract robust, domain-invariant features from limited fault data. To further enable cross-domain adaptation without costly fine-tuning, a lightweight Adapter module is introduced, which incorporates few-shot learning and online adaptation to target-domain features. Moreover, CDODF supports a continuous learning strategy that dynamically updates the model using accumulated target-domain data, ensuring long-term adaptability and diagnostic accuracy. Experimental results across scenarios, including cross-working conditions, cross-device diagnosis, and emerging fault types, show that CDODF consistently outperforms existing deep learning (DL), TL, and few-shot methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Cross-Domain Online Diagnosis Framework Under Small Fault Sample\",\"authors\":\"Zuoshuang Chen;Dongdong Zhang;Zuoyi Chen;Jun Wu;Hong-Zhong Huang\",\"doi\":\"10.1109/TIM.2025.3606021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cross-domain fault diagnosis in industrial applications presents a major challenge, especially when only a limited number of fault samples are available. Existing data-driven and transfer learning (TL) methods often struggle with real-time diagnosis, insufficient generalization across domains, and the inability to adapt to continuously evolving fault conditions. To address these limitations, this article proposes a novel cross-domain online fault diagnosis framework (CDODF). The framework leverages the contrastive language-image pretraining (CLIP) model to extract robust, domain-invariant features from limited fault data. To further enable cross-domain adaptation without costly fine-tuning, a lightweight Adapter module is introduced, which incorporates few-shot learning and online adaptation to target-domain features. Moreover, CDODF supports a continuous learning strategy that dynamically updates the model using accumulated target-domain data, ensuring long-term adaptability and diagnostic accuracy. Experimental results across scenarios, including cross-working conditions, cross-device diagnosis, and emerging fault types, show that CDODF consistently outperforms existing deep learning (DL), TL, and few-shot methods.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-12\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11151301/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151301/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Cross-Domain Online Diagnosis Framework Under Small Fault Sample
Cross-domain fault diagnosis in industrial applications presents a major challenge, especially when only a limited number of fault samples are available. Existing data-driven and transfer learning (TL) methods often struggle with real-time diagnosis, insufficient generalization across domains, and the inability to adapt to continuously evolving fault conditions. To address these limitations, this article proposes a novel cross-domain online fault diagnosis framework (CDODF). The framework leverages the contrastive language-image pretraining (CLIP) model to extract robust, domain-invariant features from limited fault data. To further enable cross-domain adaptation without costly fine-tuning, a lightweight Adapter module is introduced, which incorporates few-shot learning and online adaptation to target-domain features. Moreover, CDODF supports a continuous learning strategy that dynamically updates the model using accumulated target-domain data, ensuring long-term adaptability and diagnostic accuracy. Experimental results across scenarios, including cross-working conditions, cross-device diagnosis, and emerging fault types, show that CDODF consistently outperforms existing deep learning (DL), TL, and few-shot methods.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.