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Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator

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  • Elforjani, Mohamed
  • Bechhoefer, Eric

Abstract

The use of signal processing for condition monitoring of wind turbines data has been on-going since several decades. Failure in the analysis of high modulated data may make the machine break. An example of this is the reported real case of bearing failure on a Repower wind turbine, which could not be detected by currently applied methods. The machine had to be out of service immediately after a faulty bearing outer race was visually ascertained. Vibration dataset from this faulty machine was provided to facilitate research into wind turbines analysis and with the hope that the authors of this work can improve upon the existing techniques. In the response to this challenge, the authors of this paper proposed Spectral Kurtosis (SK) and Signal Intensity Estimator (SIE) as proven time-frequency fault indicators to tackle the question of data with different modulation rates. Extensive signal processing using time domain and time-frequency domain analysis was undertaken. It was concluded that SIE is well established mature approach and it provides a more reliable estimate of wind turbine conditions than conventional techniques such as SK, leading to better discrimination between “good” and “bad” machines.

Suggested Citation

  • Elforjani, Mohamed & Bechhoefer, Eric, 2018. "Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator," Renewable Energy, Elsevier, vol. 127(C), pages 258-268.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:258-268
    DOI: 10.1016/j.renene.2018.04.014
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    References listed on IDEAS

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    1. Shanbr, Suliman & Elasha, Faris & Elforjani, Mohamed & Teixeira, Joao, 2018. "Detection of natural crack in wind turbine gearbox," Renewable Energy, Elsevier, vol. 118(C), pages 172-179.
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    Cited by:

    1. 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.
    2. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    3. Zhao, Xueyan & Lang, Ziqiang, 2019. "Baseline model based structural health monitoring method under varying environment," Renewable Energy, Elsevier, vol. 138(C), pages 1166-1175.
    4. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    5. Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.
    6. Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.
    7. Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).

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