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标题：Long Short Term Memory-based anomaly detection applied to an industrial dosing pump
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作者：Anthony Fombonne de Galatheau ; Alexandru-Liviu Olteanu ; Nathalie Julien 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：2
页码：240-245
DOI：10.1016/j.ifacol.2022.04.200
语种：English
出版社：Elsevier
摘要：AbstractPerforming predictive maintenance in the case of equipment failures is a relatively recent and difficult topic, in part due to the heterogeneity of industrial processes but also to the increasing amounts of information that can be gathered. We investigate the topic of anomaly detection using Long Short Term Memory for failures of an industrial dosing pump. We show that we are able to obtain accurate results corresponding to real failures leading the way to set up actions in order to avoid such failures in the future.
关键词：KeywordsAnomaly detectionLong Short Term Memorysmart factoryDeep Learning