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Diagnosis and prognosis of real world wind turbine gears

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  • Elforjani, Mohamed

Abstract

Today, condition monitoring (CM) is unarguably the most important field in any industrial applications. CM of wind turbines (WT’s) has in the past few years grown substantially. Although numerous initiatives to develop CM techniques and make operations more efficient were launched, most developed tools failed to respond on time to unpredictable events. One area that shows great potential in the battle against machine damages and their exploits is the diagnosis and prognosis of WT gears. In the world of big varying modulated data, analysis of health conditions of WT gears by traditional CM methods is no longer sufficient. Example for this is the high dimensionality and very extremely modulated vibration dataset, provided by Suzlon company. Suzlon unworkably attempted to online discriminate its machines using a set of well-known CM analysis methods. However, only visual inspection could identify the faulty WT gear. Hence, Suzlon flagged up a top priority need to identify more efficient online tools for improving CM processes. In the response to this essential need, the author employs Signal Intensity Estimator (SIE) method and some machine learning (ML) algorithms to analyse Suzlon dataset. A conclusion was reached that these techniques could successfully provide a reliable estimate of WT’s conditions.

Suggested Citation

  • Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1676-1693
    DOI: 10.1016/j.renene.2019.09.109
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    References listed on IDEAS

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    1. Alvarez, Eduardo J. & Ribaric, Adrijan P., 2018. "An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA," Renewable Energy, Elsevier, vol. 115(C), pages 391-399.
    2. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    3. 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.
    4. Feng, Zhipeng & Qin, Sifeng & Liang, Ming, 2016. "Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions," Renewable Energy, Elsevier, vol. 85(C), pages 45-56.
    5. Feng, Zhipeng & Liang, Ming, 2014. "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis," Renewable Energy, Elsevier, vol. 66(C), pages 468-477.
    6. Ruiz de la Hermosa González-Carrato, Raúl, 2017. "Sound and vibration-based pattern recognition for wind turbines driving mechanisms," Renewable Energy, Elsevier, vol. 109(C), pages 262-274.
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    Cited by:

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    2. 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.
    3. Guo, Sheng & Yang, Tao & Hua, Haochen & Cao, Junwei, 2021. "Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information," Renewable Energy, Elsevier, vol. 178(C), pages 639-650.
    4. 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.
    5. 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.

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