Zhicheng Xu, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson
{"title":"Data-Driven Smart Manufacturing Technologies for Prop Shop Systems","authors":"Zhicheng Xu, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson","doi":"10.1109/SERA57763.2023.10197769","DOIUrl":null,"url":null,"abstract":"In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-term and long-term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERA57763.2023.10197769","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
In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-term and long-term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
