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Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration

Author

Listed:
  • Ehsan Mollasalehi

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

  • David Wood

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

  • Qiao Sun

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

Abstract

The idea of indicative fault diagnosis based on measuring the wind turbine tower sound and vibration is presented. It had been reported by a wind farm operator that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. The work in this paper was conceived to test whether tower top faults could be identified by taking simple measurements at the tower base. Two accelerometers were attached inside the wind turbine tower, and vibration data was collected while the wind turbine was in operation. Tower vibration signals were analyzed using Empirical Mode Decomposition and the outcomes were correlated with the vibration signals acquired directly from the generator bearings. It is shown that the generator bearing fault signatures were present in the vibrations from the tower. The results suggest that useful condition monitoring of nacelle components can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. In the second part of the paper, acoustic measurements from a healthy and a faulty wind turbine are shown. The preliminary analysis suggests that the generator bearing fault increases the overall sound pressure level at the bottom of the tower, and is not buried in the background noise.

Suggested Citation

  • Ehsan Mollasalehi & David Wood & Qiao Sun, 2017. "Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration," Energies, MDPI, vol. 10(11), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1853-:d:118577
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    References listed on IDEAS

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    1. Swanson, Laura, 2001. "Linking maintenance strategies to performance," International Journal of Production Economics, Elsevier, vol. 70(3), pages 237-244, April.
    2. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    3. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
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    Cited by:

    1. Francisco Haces-Fernandez, 2021. "Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy," Energies, MDPI, vol. 14(22), pages 1-19, November.
    2. Jong-Yih Kuo & Shang-Yi You & Hui-Chi Lin & Chao-Yang Hsu & Baiying Lei, 2022. "Constructing Condition Monitoring Model of Wind Turbine Blades," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    3. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    4. Muhammad Amir Khan & Bilal Asad & Karolina Kudelina & Toomas Vaimann & Ants Kallaste, 2022. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art," Energies, MDPI, vol. 16(1), pages 1-54, December.
    5. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    6. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    7. Jijian Lian & Hongzhen Wang & Haijun Wang, 2018. "Study on Vibration Transmission among Units in Underground Powerhouse of a Hydropower Station," Energies, MDPI, vol. 11(11), pages 1-22, November.

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