文章基本信息

标题：Visual Analytics: A New Paradigm for Process Monitoring
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作者：Ibrahim Yousef ; Sirish L. Shah ; R. Bhushan Gopaluni 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：7
页码：376-383
DOI：10.1016/j.ifacol.2022.07.473
语种：English
出版社：Elsevier
摘要：AbstractAs a result of recent breakthroughs in computer vision technologies, significant research interest has emerged to encode process data into visual clues and treat process monitoring problems as computer vision tasks. Imaging time-series signals as a feature engineering step forms a new branch of data analytics called ”visual analytics”. In the context of process monitoring, we define visual analytics as the integration of visual representation of the data combined with the use of computer vision tools and analytical reasoning to support decision-making and knowledge extraction from the data. In this work, a novel end-to-end visual analytics pipeline for industrial process fault detection using 1D and 2D convolution operations is proposed. The proposed approach presents a visual representation of data that captures temporal and local features from historical time-series signals. Next, the learned features in a 2D format are visually recognized and classified using 2D convolution operations. Our experimental results demonstrate that this approach achieves better performance on an industrial multivariate dataset compared to other state-of-art signals imaging tools such as Gramian Angular Field (GAF) and Recurrence Plots (RP).
关键词：KeywordsProcess MonitoringSignal ProcessingDeep LearningImage AnalysisFault DetectionVisionApplications