{"title":"制造系统的数据分析","authors":"A. Vodencarevic, T. Fett","doi":"10.1109/ETFA.2015.7301541","DOIUrl":null,"url":null,"abstract":"Data analytics plays one of the key roles in building intelligent systems, which bring automation to the new level of safety, reliability and efficiency, at the same time lowering the perceived complexity for the user. In this paper, we present the goals of data analytics in manufacturing and illustrate several application scenarios we have successfully worked on at Reifenhäuser REICOFIL GmbH & Co. KG. These include process monitoring and anomaly detection using virtual sensors, root cause analysis, plant simulation and optimization, assessing trade-offs between product quality criteria and extracting knowledge from data. Furthermore, we list a number of challenges that data analytics typically faces in manufacturing environments, demonstrating them on several concrete examples.","PeriodicalId":6862,"journal":{"name":"2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)","volume":"7 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Data analytics for manufacturing systems\",\"authors\":\"A. Vodencarevic, T. Fett\",\"doi\":\"10.1109/ETFA.2015.7301541\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data analytics plays one of the key roles in building intelligent systems, which bring automation to the new level of safety, reliability and efficiency, at the same time lowering the perceived complexity for the user. In this paper, we present the goals of data analytics in manufacturing and illustrate several application scenarios we have successfully worked on at Reifenhäuser REICOFIL GmbH & Co. KG. These include process monitoring and anomaly detection using virtual sensors, root cause analysis, plant simulation and optimization, assessing trade-offs between product quality criteria and extracting knowledge from data. Furthermore, we list a number of challenges that data analytics typically faces in manufacturing environments, demonstrating them on several concrete examples.\",\"PeriodicalId\":6862,\"journal\":{\"name\":\"2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)\",\"volume\":\"7 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2015.7301541\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2015.7301541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data analytics plays one of the key roles in building intelligent systems, which bring automation to the new level of safety, reliability and efficiency, at the same time lowering the perceived complexity for the user. In this paper, we present the goals of data analytics in manufacturing and illustrate several application scenarios we have successfully worked on at Reifenhäuser REICOFIL GmbH & Co. KG. These include process monitoring and anomaly detection using virtual sensors, root cause analysis, plant simulation and optimization, assessing trade-offs between product quality criteria and extracting knowledge from data. Furthermore, we list a number of challenges that data analytics typically faces in manufacturing environments, demonstrating them on several concrete examples.