Next Article in Journal
Metal-Organic Hybrid Metamaterials for Spectral-Band Selective Active Terahertz Modulators
Next Article in Special Issue
Transient Modeling and Recovery of Non-Stationary Fault Signature for Condition Monitoring of Induction Motors
Previous Article in Journal
Silhouettes from Real Objects Enable Realistic Interactions with a Virtual Human in Mobile Augmented Reality
Previous Article in Special Issue
Transfer Learning-Based Fault Diagnosis under Data Deficiency
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 6
10.3390/app11062761
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

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

by
Karolina Kudelina
*,
Toomas Vaimann
,
Bilal Asad
,
Anton Rassõlkin
,
Ants Kallaste
and
Galina Demidova
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(6), 2761; https://doi.org/10.3390/app11062761
Submission received: 22 February 2021 / Revised: 16 March 2021 / Accepted: 17 March 2021 / Published: 19 March 2021
(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)
Browse Figures
Review Reports Versions Notes

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.
Keywords: fault diagnostics; machine learning; artificial intelligence; pattern recognition; neural networks fault diagnostics; machine learning; artificial intelligence; pattern recognition; neural networks

Share and Cite

MDPI and ACS Style

Kudelina, K.; Vaimann, T.; Asad, B.; Rassõlkin, A.; Kallaste, A.; Demidova, G. Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning. Appl. Sci. 2021, 11, 2761. https://doi.org/10.3390/app11062761

AMA Style

Kudelina K, Vaimann T, Asad B, Rassõlkin A, Kallaste A, Demidova G. Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning. Applied Sciences. 2021; 11(6):2761. https://doi.org/10.3390/app11062761

Chicago/Turabian Style

Kudelina, Karolina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, and Galina Demidova. 2021. "Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning" Applied Sciences 11, no. 6: 2761. https://doi.org/10.3390/app11062761

APA Style

Kudelina, K., Vaimann, T., Asad, B., Rassõlkin, A., Kallaste, A., & Demidova, G. (2021). Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning. Applied Sciences, 11(6), 2761. https://doi.org/10.3390/app11062761

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop