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Methods of Condition Monitoring and Fault Detection for Electrical Machines

Author

Listed:
  • Karolina Kudelina

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Bilal Asad

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Toomas Vaimann

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Anton Rassõlkin

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Ants Kallaste

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Huynh Van Khang

    (Department of Engineering Sciences, University of Agder, 4604 Kristiansand, Norway)

Abstract

Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced.

Suggested Citation

  • Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7459-:d:674925
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    References listed on IDEAS

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    Cited by:

    1. Jose R. Huerta-Rosales & David Granados-Lieberman & Juan P. Amezquita-Sanchez & Arturo Garcia-Perez & Maximiliano Bueno-Lopez & Martin Valtierra-Rodriguez, 2022. "Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions," Energies, MDPI, vol. 15(22), pages 1-15, November.
    2. Hoffstaedt, J.P. & Truijen, D.P.K. & Fahlbeck, J. & Gans, L.H.A. & Qudaih, M. & Laguna, A.J. & De Kooning, J.D.M. & Stockman, K. & Nilsson, H. & Storli, P.-T. & Engel, B. & Marence, M. & Bricker, J.D., 2022. "Low-head pumped hydro storage: A review of applicable technologies for design, grid integration, control and modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Sebastian Berhausen & Tomasz Jarek, 2022. "Analysis of Impact of Design Solutions of an Electric Machine with Permanent Magnets for Bearing Voltages with Inverter Power Supply," Energies, MDPI, vol. 15(12), pages 1-19, June.
    4. Toomas Vaimann & Jose Alfonso Antonino-Daviu & Anton Rassõlkin, 2023. "Novel Approaches to Electrical Machine Fault Diagnosis," Energies, MDPI, vol. 16(15), pages 1-4, July.
    5. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    6. Hadi Ashraf Raja & Karolina Kudelina & Bilal Asad & Toomas Vaimann & Ants Kallaste & Anton Rassõlkin & Huynh Van Khang, 2022. "Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines," Energies, MDPI, vol. 15(24), pages 1-16, December.
    7. Ahmed Belkhadir & Remus Pusca & Driss Belkhayat & Raphaël Romary & Youssef Zidani, 2023. "Analytical Modeling, Analysis and Diagnosis of External Rotor PMSM with Stator Winding Unbalance Fault," Energies, MDPI, vol. 16(7), pages 1-23, April.
    8. Mikko Tahkola & Áron Szücs & Jari Halme & Akhtar Zeb & Janne Keränen, 2022. "A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study," Energies, MDPI, vol. 15(9), pages 1-23, May.
    9. Viktor Rjabtšikov & Anton Rassõlkin & Karolina Kudelina & Ants Kallaste & Toomas Vaimann, 2023. "Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis," Energies, MDPI, vol. 16(19), pages 1-17, October.
    10. Krzysztof Kolano & Artur Jan Moradewicz & Bartosz Drzymała & Jakub Gęca, 2022. "Influence of the Placement Accuracy of the Brushless DC Motor Hall Sensor on Inverter Transistor Losses," Energies, MDPI, vol. 15(5), pages 1-13, March.

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