{"title":"基于多源信息的隐式线性维纳退化过程剩余使用寿命预测","authors":"Jiaxin Yang, Shengjin Tang, Pengya Fang, Fengfei Wang, Xiaoyan Sun, Xiaosheng Si","doi":"10.1177/1748006x221132606","DOIUrl":null,"url":null,"abstract":"Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority of the proposed method.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information\",\"authors\":\"Jiaxin Yang, Shengjin Tang, Pengya Fang, Fengfei Wang, Xiaoyan Sun, Xiaosheng Si\",\"doi\":\"10.1177/1748006x221132606\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority of the proposed method.\",\"PeriodicalId\":51266,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2022-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/1748006x221132606\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/1748006x221132606","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information
Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority of the proposed method.
期刊介绍:
The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome