{"title":"工业过程控制的大数据分析","authors":"A. R. Khan, H. Schiøler, M. Kulahci, T. Knudsen","doi":"10.1109/ETFA.2017.8247658","DOIUrl":null,"url":null,"abstract":"Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.","PeriodicalId":6522,"journal":{"name":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"100 6","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Big data analytics for industrial process control\",\"authors\":\"A. R. Khan, H. Schiøler, M. Kulahci, T. Knudsen\",\"doi\":\"10.1109/ETFA.2017.8247658\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.\",\"PeriodicalId\":6522,\"journal\":{\"name\":\"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"volume\":\"100 6\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2017.8247658\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2017.8247658","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.
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