{"title":"Reservoir computing-based slow feature analysis: Application in fault classification","authors":"Alireza Memarian , Amirreza Memarian , Seshu Kumar Damarla , Rahul Raveendran , Biao Huang","doi":"10.1016/j.ifacol.2024.08.378","DOIUrl":null,"url":null,"abstract":"<div><p>Differentiating between various types of faults and classifying them based on their importance is essential for process fault detection and diagnosis. This classification helps operators to prioritize their actions based on the severity of the faults. This paper proposes a reservoir computing-based slow feature analysis (RCSFA) to model complex and nonlinear industrial processes and study its application in fault classification while integrated with a graph neural network (GNN) and majority voting ensemble causality detection. To make the algorithm robust to unseen faults, real-time operator feedback is included by utilizing operator eye tracking. The practical applicability of the proposed method and its application in fault classification is studied through an industrial application.</p></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"58 14","pages":"Pages 452-457"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405896324011376/pdf?md5=542c9208401361451ae59b0990dc4c5f&pid=1-s2.0-S2405896324011376-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFAC-PapersOnLine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405896324011376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Differentiating between various types of faults and classifying them based on their importance is essential for process fault detection and diagnosis. This classification helps operators to prioritize their actions based on the severity of the faults. This paper proposes a reservoir computing-based slow feature analysis (RCSFA) to model complex and nonlinear industrial processes and study its application in fault classification while integrated with a graph neural network (GNN) and majority voting ensemble causality detection. To make the algorithm robust to unseen faults, real-time operator feedback is included by utilizing operator eye tracking. The practical applicability of the proposed method and its application in fault classification is studied through an industrial application.
期刊介绍：
All papers from IFAC meetings are published, in partnership with Elsevier, the IFAC Publisher, in theIFAC-PapersOnLine proceedings series hosted at the ScienceDirect web service. This series includes papers previously published in the IFAC website.The main features of the IFAC-PapersOnLine series are: -Online archive including papers from IFAC Symposia, Congresses, Conferences, and most Workshops. -All papers accepted at the meeting are published in PDF format - searchable and citable. -All papers published on the web site can be cited using the IFAC PapersOnLine ISSN and the individual paper DOI (Digital Object Identifier). The site is Open Access in nature - no charge is made to individuals for reading or downloading. Copyright of all papers belongs to IFAC and must be referenced if derivative journal papers are produced from the conference papers. All papers published in IFAC-PapersOnLine have undergone a peer review selection process according to the IFAC rules.
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