Yulu Guo;Hui Shi;Zengshou Dong;Yujia Zheng;Chaoli Sun
{"title":"RUL Prediction of Adaptive Kernel Particle Filtering Based on Time-Varying Fading Factor Considering Dynamic External Environment","authors":"Yulu Guo;Hui Shi;Zengshou Dong;Yujia Zheng;Chaoli Sun","doi":"10.1109/TIM.2025.3604111","DOIUrl":null,"url":null,"abstract":"Dynamic environmental factors often introduce uncertainty into the degradation process of equipment, complicating remaining useful life (RUL) prediction. To address this challenge, the Wiener degradation process considering the impact of external dynamic environments is modeled, and a dynamic system filtering method based on a Bayesian framework is proposed for online RUL prediction. First, the Wiener process is constructed, accounting for the effects of dynamic environmental factors on the degradation rate and the correlation between the degradation rate and volatility. Then, to solve the uncertainty problem in the filtering process, a time-varying fading factor is introduced in the dynamic filtering process to adjust particle states in real time and improve the ability to track state changes. The nonparametric adaptive kernel density estimation (AKDE) is employed to approximate the true posterior probability distribution, and the discrete particle samples are converted into continuous probability density functions (PDFs), which increases the diversity of particles and optimizes the resampling strategy. Finally, the feasibility and effectiveness of the proposed model and algorithm are validated through a simulation study and the application of a lithium-ion battery.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9000,"publicationDate":"2025-08-29","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/11145243/","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
Dynamic environmental factors often introduce uncertainty into the degradation process of equipment, complicating remaining useful life (RUL) prediction. To address this challenge, the Wiener degradation process considering the impact of external dynamic environments is modeled, and a dynamic system filtering method based on a Bayesian framework is proposed for online RUL prediction. First, the Wiener process is constructed, accounting for the effects of dynamic environmental factors on the degradation rate and the correlation between the degradation rate and volatility. Then, to solve the uncertainty problem in the filtering process, a time-varying fading factor is introduced in the dynamic filtering process to adjust particle states in real time and improve the ability to track state changes. The nonparametric adaptive kernel density estimation (AKDE) is employed to approximate the true posterior probability distribution, and the discrete particle samples are converted into continuous probability density functions (PDFs), which increases the diversity of particles and optimizes the resampling strategy. Finally, the feasibility and effectiveness of the proposed model and algorithm are validated through a simulation study and the application of a lithium-ion battery.
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.