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dc.contributor.authorBounoua, Wahiba
dc.contributor.authorAftab, Muhammad Faisal
dc.contributor.authorOmlin, Christian Walter Peter
dc.date.accessioned2024-12-09T11:34:25Z
dc.date.available2024-12-09T11:34:25Z
dc.date.created2023-11-24T12:36:13Z
dc.date.issued2023
dc.identifier.citationBounoua, W., Aftab, M. F. & Omlin, C. W. P. (2023). Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks. IFAC-PapersOnLine, 56 (2), 11687-11692.en_US
dc.identifier.issn2405-8963
dc.identifier.urihttps://hdl.handle.net/11250/3168858
dc.description.abstractValve stiction is one of the major causes of poorly performing industrial control loops. Stiction occurs when the static friction exceeds the dynamic friction during a direction change or when the stem is at rest. Recently, machine learning techniques were employed to detect the presence of stiction. These techniques required the use of multiple signals from the control loop in order to extract the key features to distinguish stiction cases from healthy or other malfunctions cases. In this paper, a new image-generating method, named the Poincaré plot, is proposed to feed the convolutional neural network (CNN) that only needs one signal from the control loop. The Poincaré plot is a powerful technique that can reveal the complexity of the process by evaluating the correlation within a single time series. The proposed Poincaré plot-based CNN showed satisfactory results in detecting stiction in real industrial applications as compared to other machine learning techniques present in the literature.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleStiction detection in industrial control valves using Poincaré plot-based convolutional neural networksen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.pagenumber11687-11692en_US
dc.source.volume56en_US
dc.source.journalIFAC-PapersOnLineen_US
dc.source.issue2en_US
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2023.10.523
dc.identifier.cristin2201655
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal