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dc.contributor.authorSunal, Cem Ekin
dc.contributor.authorDyo, Vladimir
dc.contributor.authorVelisavljevic, Vladan
dc.contributor.illustrator
dc.date.accessioned2022-08-03T11:30:20Z
dc.date.available2022-06-23T00:00:00Z
dc.date.available2022-08-03T11:30:20Z
dc.date.issued2022-07-01
dc.identifier.citationSunal CE, Dyo V, Velisavljevic V (2022) 'Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors', IEEE Access, 10 (), pp.71344-71355.en_US
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2022.3187718
dc.identifier.urihttp://hdl.handle.net/10547/625472
dc.description.abstractCentrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.en_US
dc.description.sponsorshipInnovate UK KTP granten_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/9812600en_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcentrifugal pumpsen_US
dc.subjectfault diagnosisen_US
dc.subjectinduction motorsen_US
dc.subjectmachine ;earningen_US
dc.subjectmotor current signature analysisen_US
dc.subjectsignal processingen_US
dc.titleReview of machine learning based fault detection for centrifugal pump induction motorsen_US
dc.typeArticleen_US
dc.identifier.eissn2169-3536
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.identifier.journalIEEE Accessen_US
dc.date.updated2022-08-03T11:12:35Z
dc.description.noteJournal is gold OA, have added VoR file to RMAS record.
refterms.dateFOA2022-08-03T11:30:20Z


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