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Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox

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  • Xin, Ge
  • Hamzaoui, Nacer
  • Antoni, Jérôme

Abstract

The diagnosis of gearboxes plays a crucial role in the maintenance of wind turbine. Considering critical elements – i.e. gears and bearings – of gear set, the effective and exact identification of fault sources is appealing yet challenging in complex mechanical systems. Although rotating machine signals are perfectly modelled as cyclostationary (CS) processes, very few researches have so far tried to refine single CS component of interest from a mixture of multi sources; thus the efficacy of classical vibrodiagnostic tool (e.g. envelope analysis) can be greatly enhanced in a wide variety of situations, e.g. poly-cyclostationary cases in wind turbine gearboxes. As such, this paper exploits the statistical behavior of CS signals using a stochastic model based on a periodic variance to extract more specific information from the data themselves. In particular, a statistical indicator is proposed to assess the strength of CS components as well as a full-band time-dependent filter to recover the pure CS signals in the time domain. This proves very useful in many situations where the characteristic components of gears and/or bearings are embedded in heavy background noise that jeopardize their detection in practical applications. The derivation of the proposed scheme is described in detail. Its effectiveness is finally demonstrated with both synthetic and experimental examples.

Suggested Citation

  • Xin, Ge & Hamzaoui, Nacer & Antoni, Jérôme, 2020. "Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox," Renewable Energy, Elsevier, vol. 147(P1), pages 1739-1758.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1739-1758
    DOI: 10.1016/j.renene.2019.09.087
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    References listed on IDEAS

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    1. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    2. 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.
    3. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
    4. Romero, Antonio & Soua, Slim & Gan, Tat-Hean & Wang, Bin, 2018. "Condition monitoring of a wind turbine drive train based on its power dependant vibrations," Renewable Energy, Elsevier, vol. 123(C), pages 817-827.
    5. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    6. 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.
    7. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    8. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
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