Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions...

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Veröffentlicht in:Applied sciences 2021-03, Vol.11 (6), p.2761
Hauptverfasser: Kudelina, Karolina, Vaimann, Toomas, Asad, Bilal, Rassõlkin, Anton, Kallaste, Ants, Demidova, Galina
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Sprache:eng
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Zusammenfassung:A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11062761