{"title":"A Prototype Learning Framework Based on Continual Learning for Motor Incremental Fault Diagnosis Under Few-Shot Conditions","authors":"Heng Shan;Xiaofei Zhang;Weizhi Liang;Zeping Wu;Haidong Shao;Guojun Qin","doi":"10.1109/TIM.2025.3604938","DOIUrl":null,"url":null,"abstract":"In the actual industrial scenario, motor fault classes gradually increase, and the fault data need to be acquired incrementally during the service. Continual learning (CL) has been introduced to the field of fault diagnosis (FD), aiming at constructing an FD model with continuous evolution capability. However, the current research has the following limitations: 1) assigning parameters for each task independently leads to a linear increase in model complexity and 2) it relies heavily on the quality and completeness of historical data and the research on incremental FD under few-shot conditions is not sufficient. To address the above limitations, this article proposes a prototype learning framework for motor incremental FD (PLFIFD) under few-shot conditions. First, a prototype classifier based on distance metric is proposed to avoid uncontrollable changes due to the update of parameters in the output layer. The prototype set is dynamically expanded instead of assigning network parameters individually. Second, a multiobjective loss function is designed to jointly optimize the classification boundary and prototype space distribution to enhance intraclass compactness and interclass separability and to improve the generalization ability under few-shot conditions and stability in dynamic scenarios. Finally, the effectiveness of PLFIFD is verified on induction motor (IM) and permanent magnet synchronous motor (PMSM).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-02","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/11146825/","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}
引用次数: 0
Abstract
In the actual industrial scenario, motor fault classes gradually increase, and the fault data need to be acquired incrementally during the service. Continual learning (CL) has been introduced to the field of fault diagnosis (FD), aiming at constructing an FD model with continuous evolution capability. However, the current research has the following limitations: 1) assigning parameters for each task independently leads to a linear increase in model complexity and 2) it relies heavily on the quality and completeness of historical data and the research on incremental FD under few-shot conditions is not sufficient. To address the above limitations, this article proposes a prototype learning framework for motor incremental FD (PLFIFD) under few-shot conditions. First, a prototype classifier based on distance metric is proposed to avoid uncontrollable changes due to the update of parameters in the output layer. The prototype set is dynamically expanded instead of assigning network parameters individually. Second, a multiobjective loss function is designed to jointly optimize the classification boundary and prototype space distribution to enhance intraclass compactness and interclass separability and to improve the generalization ability under few-shot conditions and stability in dynamic scenarios. Finally, the effectiveness of PLFIFD is verified on induction motor (IM) and permanent magnet synchronous motor (PMSM).
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.