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Sound and vibration-based pattern recognition for wind turbines driving mechanisms

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  • Ruiz de la Hermosa González-Carrato, Raúl

Abstract

This paper proposes a pattern recognition approach for a Fault Detection and Diagnosis (FDD) system based on the wavelet and the fast Fourier transform. Both techniques are developed in an experimental set that simulates the driving mechanisms housed in the nacelle of a wind turbine (WT) with results being validated in a real wind farm. After a first separate approach of the vibration harmonics and the sound energy, the root mean square error (RMSE) is used to fuse the data into a common pattern. The pattern reveals accurate information for unstable features (e.g. the case of the sound) related to misalignments among other failures. Comparing the experiments with the pattern, it is observed that the pattern is often close to the induced failures with minor exceptions. Relations among all the measured points are also found. The usefulness of the findings lies in the possibility of monitoring inaccessible devices considering this relation. Cost savings based on the strategic placement of the sensors can be intended too. The FDD will ensure the implementation of predictive actions before the occurrence of a catastrophic failure in an area where there is an ongoing challenge for being competitive.

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  • 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.
    • Handle: RePEc:eee:renene:v:109:y:2017:i:c:p:262-274
    DOI: 10.1016/j.renene.2017.03.042
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    References listed on IDEAS

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    1. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
    2. Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    3. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    4. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
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    6. Soua, Slim & Van Lieshout, Paul & Perera, Asanka & Gan, Tat-Hean & Bridge, Bryan, 2013. "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring," Renewable Energy, Elsevier, vol. 51(C), pages 175-181.
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    Cited by:

    1. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Song, Chaosheng & Chen, Dingliang & Zheng, Jie, 2022. "Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion," Renewable Energy, Elsevier, vol. 200(C), pages 1023-1036.
    2. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    3. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    4. Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.

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