Novel Approaches to Electrical Machine Fault Diagnosis-Reference-Cited by-同舟云学术

Novel Approaches to Electrical Machine Fault Diagnosis

Published:2023-07-27 Issue:15 Volume:16 Page:5641
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Vaimann Toomas¹^ORCID,Antonino-Daviu Jose Alfonso²^ORCID,Rassõlkin Anton¹^ORCID

Affiliation:

1. Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia

2. Instituto Tecnológico de la Energia, Universitat Politecnica de Valencia (UPV), Camino de Vera s/n, 46022 Valencia, Spain

Abstract

The increasing demand for intelligent machines, coupled with the drive for the more efficient utilization of these machines in various industries, and the emergence of Industry 4 [...]

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Link

https://www.mdpi.com/1996-1073/16/15/5641/pdf

Reference8 articles.

1. Kudelina, K., Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A., and Khang, H.V. (2021). Methods of Condition Monitoring and Fault Detection for Electrical Machines. Energies, 14.

2. Gu, B.-G. (2022). Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square. Energies, 15.

3. Tahkola, M., Szücs, Á., Halme, J., Zeb, A., and Keränen, J. (2022). A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study. Energies, 15.

4. Im, S.-H., and Gu, B.-G. (2022). Study of Induction Motor Inter-Turn Fault Part I: Development of Fault Models with Distorted Flux Representation. Energies, 15.

5. Im, S.-H., and Gu, B.-G. (2022). Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method. Energies, 15.