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Machine Learning for Inverter-Fed Motors Monitoring and Fault Detection: An Overview

dc.contributor.authorGarcía Pérez, Diego 
dc.contributor.authorSaeed Hazkial Gerges, Mariam 
dc.contributor.authorDíaz Blanco, Ignacio 
dc.contributor.authorEnguita González, José María 
dc.contributor.authorGuerrero Muñoz, Juan Manuel 
dc.contributor.authorBriz del Blanco, Fernando 
dc.date.accessioned2024-05-10T08:39:26Z
dc.date.available2024-05-10T08:39:26Z
dc.date.issued2024
dc.identifier.citationIEEE Access, 12, p. 27167-27179 (2024); doi:10.1109/ACCESS.2024.3366810
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10651/72498
dc.description.abstractMonitoring and fault detection can be critical for efficient, safe and reliable operation of electric drive systems. Unfortunately, developing accurate physics-based models for these tasks is difficult due to unknown machine parameters and incomplete knowledge of the physical phenomena occurring within the system. Machine Learning (ML) methods can learn the system’s behavior from data without requiring explicit models. However, expert knowledge of the system is still crucial to extract useful features before applying ML models. This paper presents an overview of the use of ML and data visualization methods for condition monitoring of inverter fed induction motors. More specifically, stator winding temperature estimation and insulation degradation are considered. The analyzed methods make use of the signals normally available in electric drives. Time and frequency-based approaches are considered. The developed methods are assessed on an experimental test bench. The paper is intended to bridge ML and electric drive domains. The desired outcome of this work is to provide useful guidelines for researchers in the electric drives field who aim to apply modern ML and data visualization techniques for monitoring and fault detection.spa
dc.format.extentp. 27167-27179spa
dc.language.isoengspa
dc.publisherIEEEspa
dc.relation.ispartofIEEE Access, 12spa
dc.rightsAtribución 4.0 Internacional*
dc.rights© 2024 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learning (ML)spa
dc.subjectInverter-fed motorsspa
dc.subjectFault detectionspa
dc.subjectInsulation monitoringspa
dc.titleMachine Learning for Inverter-Fed Motors Monitoring and Fault Detection: An Overviewspa
dc.typejournal articlespa
dc.identifier.doi10.1109/ACCESS.2024.3366810
dc.relation.publisherversionhttp://doi.org/10.1109/ACCESS.2024.3366810spa
dc.rights.accessRightsopen access
dc.type.hasVersionVoRspa


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