Methods of Condition Monitoring and Fault Detection for Electrical Machines

Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition mo...

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Autores principales:	Karolina Kudelina, Bilal Asad, Toomas Vaimann, Anton Rassõlkin, Ants Kallaste, Huynh Van Khang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence condition monitoring failure detection fault diagnosis fuzzy logic machine learning Technology T
Acceso en línea:	https://doaj.org/article/4a81f96a4546476f9b3935f4626f80f2
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spelling	oai:doaj.org-article:4a81f96a4546476f9b3935f4626f80f22021-11-25T17:25:42ZMethods of Condition Monitoring and Fault Detection for Electrical Machines10.3390/en142274591996-1073https://doaj.org/article/4a81f96a4546476f9b3935f4626f80f22021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7459https://doaj.org/toc/1996-1073Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced.Karolina KudelinaBilal AsadToomas VaimannAnton RassõlkinAnts KallasteHuynh Van KhangMDPI AGarticleartificial intelligencecondition monitoringfailure detectionfault diagnosisfuzzy logicmachine learningTechnologyTENEnergies, Vol 14, Iss 7459, p 7459 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence condition monitoring failure detection fault diagnosis fuzzy logic machine learning Technology T
spellingShingle	artificial intelligence condition monitoring failure detection fault diagnosis fuzzy logic machine learning Technology T Karolina Kudelina Bilal Asad Toomas Vaimann Anton Rassõlkin Ants Kallaste Huynh Van Khang Methods of Condition Monitoring and Fault Detection for Electrical Machines
description	Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced.
format	article
author	Karolina Kudelina Bilal Asad Toomas Vaimann Anton Rassõlkin Ants Kallaste Huynh Van Khang
author_facet	Karolina Kudelina Bilal Asad Toomas Vaimann Anton Rassõlkin Ants Kallaste Huynh Van Khang
author_sort	Karolina Kudelina
title	Methods of Condition Monitoring and Fault Detection for Electrical Machines
title_short	Methods of Condition Monitoring and Fault Detection for Electrical Machines
title_full	Methods of Condition Monitoring and Fault Detection for Electrical Machines
title_fullStr	Methods of Condition Monitoring and Fault Detection for Electrical Machines
title_full_unstemmed	Methods of Condition Monitoring and Fault Detection for Electrical Machines
title_sort	methods of condition monitoring and fault detection for electrical machines
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4a81f96a4546476f9b3935f4626f80f2
work_keys_str_mv	AT karolinakudelina methodsofconditionmonitoringandfaultdetectionforelectricalmachines AT bilalasad methodsofconditionmonitoringandfaultdetectionforelectricalmachines AT toomasvaimann methodsofconditionmonitoringandfaultdetectionforelectricalmachines AT antonrassolkin methodsofconditionmonitoringandfaultdetectionforelectricalmachines AT antskallaste methodsofconditionmonitoringandfaultdetectionforelectricalmachines AT huynhvankhang methodsofconditionmonitoringandfaultdetectionforelectricalmachines
_version_	1718412347658207232

Methods of Condition Monitoring and Fault Detection for Electrical Machines

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