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Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Author

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  • Hadi Ashraf Raja

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

  • Karolina Kudelina

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

  • Bilal Asad

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

  • Toomas Vaimann

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

  • Ants Kallaste

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

  • Anton Rassõlkin

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

  • Huynh Van Khang

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

Abstract

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9507-:d:1004016
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Siddique Akbar & Toomas Vaimann & Bilal Asad & Ants Kallaste & Muhammad Usman Sardar & Karolina Kudelina, 2023. "State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions," Energies, MDPI, vol. 16(17), pages 1-44, September.
    2. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    3. 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.

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