Zhicheng Xu, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson
{"title":"用于道具商店系统的数据驱动智能制造技术","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":"{\"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}","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}
Data-Driven Smart Manufacturing Technologies for Prop Shop Systems
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
