Yulin Sun , Shouqiang Kang , Yujing Wang , Liansheng Liu , Wenmin Lv , Hongqi Wang
{"title":"Fault diagnosis method for harmonic reducer based on personalized federated aggregation strategy with skip cycle weight","authors":"Yulin Sun , Shouqiang Kang , Yujing Wang , Liansheng Liu , Wenmin Lv , Hongqi Wang","doi":"10.1016/j.measurement.2024.116275","DOIUrl":null,"url":null,"abstract":"<div><div>In order to solve the problem of low fault diagnosis accuracy caused by the difference in data distribution between users of different harmonic reducers under data islands, a privacy-preserving fault diagnosis method for harmonic reducers based on personalized federated learning (PFL-HR) is proposed. First, a model construction method based on second aggregation is proposed to deploy personalized local models among users, reducing differences in data distribution. Second, a federated aggregation strategy based on cycle weight is proposed to update the global model parameters, accelerating the convergence of the global model. Finally, in the global model parameters distribution stage, a model parameters’ skip aggregation strategy is proposed to extend the training paradigm, further improving diagnosis accuracy. Through multiple groups of experiments on the harmonic reducer data collected from the self-built experimental platform, the results show that PFL-HR improves accuracy by an average of 6.08%. compared to other personalized federated learning methods.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116275"},"PeriodicalIF":5.2000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263224124021602","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In order to solve the problem of low fault diagnosis accuracy caused by the difference in data distribution between users of different harmonic reducers under data islands, a privacy-preserving fault diagnosis method for harmonic reducers based on personalized federated learning (PFL-HR) is proposed. First, a model construction method based on second aggregation is proposed to deploy personalized local models among users, reducing differences in data distribution. Second, a federated aggregation strategy based on cycle weight is proposed to update the global model parameters, accelerating the convergence of the global model. Finally, in the global model parameters distribution stage, a model parameters’ skip aggregation strategy is proposed to extend the training paradigm, further improving diagnosis accuracy. Through multiple groups of experiments on the harmonic reducer data collected from the self-built experimental platform, the results show that PFL-HR improves accuracy by an average of 6.08%. compared to other personalized federated learning methods.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.