{"title":"A Model Optimization Method for Accelerated Degradation Test Data","authors":"Xiaobing Li, Guangze Pan, Jun Ying, Xiaocui Zhu","doi":"10.1109/ICSRS48664.2019.8987669","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of fitting error in accelerated degradation data processing, a model optimization method was proposed. Firstly, the best fitting model was selected according to the minimum Residual Sum of Squares, and pseudo-failure lifetime was calculated for each degradation data group. Secondly, the optimization method of the lifetime distribution model was proposed and the best lifetime distribution was obtained according to the minimum fitting error. Finally, a case example was given, which indicated that the deviation may occurred without the model optimization.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th International Conference on System Reliability and Safety (ICSRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSRS48664.2019.8987669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Aiming at the problem of fitting error in accelerated degradation data processing, a model optimization method was proposed. Firstly, the best fitting model was selected according to the minimum Residual Sum of Squares, and pseudo-failure lifetime was calculated for each degradation data group. Secondly, the optimization method of the lifetime distribution model was proposed and the best lifetime distribution was obtained according to the minimum fitting error. Finally, a case example was given, which indicated that the deviation may occurred without the model optimization.