{"title":"统计数据驱动的剩余使用寿命预测——基于Wiener过程的方法综述","authors":"Qingluan Guan, Xiukun Wei","doi":"10.1109/PHM58589.2023.00020","DOIUrl":null,"url":null,"abstract":"Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Statistical Data-driven Remaining Useful Life Prediction—A Review on the Wiener Process-based Method\",\"authors\":\"Qingluan Guan, Xiukun Wei\",\"doi\":\"10.1109/PHM58589.2023.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.\",\"PeriodicalId\":196601,\"journal\":{\"name\":\"2023 Prognostics and Health Management Conference (PHM)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Prognostics and Health Management Conference (PHM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PHM58589.2023.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Prognostics and Health Management Conference (PHM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PHM58589.2023.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Statistical Data-driven Remaining Useful Life Prediction—A Review on the Wiener Process-based Method
Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.