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

Year: 2021

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app11062761

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

Institution: Tallinn University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,160