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

Karolina Kudelina*, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, Galina Demidova

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number2761
Number of pages19
JournalAPPLIED SCIENCES
Volume11
Issue number6
DOIs
Publication statusPublished - 19 Mar 2021
MoE publication typeA2 Review article in a scientific journal

Keywords

  • fault diagnostics
  • machine learning
  • artificial intellegence
  • pattern recognition
  • neural networks

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