文章基本信息

标题：A data-driven framework to deal with intrinsic variability of industrial processes: An application in the textile industry
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作者：Patrice Lajoie ; Jonathan Gaudreault ; Nadia Lehoux 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：13
页码：731-736
DOI：10.1016/j.ifacol.2019.11.202
语种：English
出版社：Elsevier
摘要：In many industries (e.g. natural resources processing, food processing, etc.), variation is intrinsic to the process. Data captured by advanced technologies and sensors can drive in-depth reflection to avoid and resolve potential issues related to process variability. In this study, we offer practical steps as a guidance for production engineers to deal with the intrinsic variability of industrial processes. We propose a methodological framework in three steps, which includes the definition and the characterization of the industrial process, predictive modeling and prescriptive analytics. In addition, we illustrate the framework application with a real case study from the textile industry.
关键词：KeywordsProbabilisticstatistical models in industrial plant controlIndustrialapplied mathematics for productionIndustry 4.0intrinsic variabilitydata analytics