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A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis

Author

Listed:
  • Li, Jimeng
  • Chen, Xuefeng
  • Du, Zhaohui
  • Fang, Zuowei
  • He, Zhengjia

Abstract

Condition monitoring of a wind turbine is important to extend the wind turbine system's reliability and useful life. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear systems, can detect weak signals overwhelmed in the noise. Therefore, a new noise-controlled second-order enhanced SR method based on the Morlet wavelet transform is proposed to extract fault feature for wind turbine vibration signals in the present study. The second-order SR method can obtain better denoising effect and higher signal-to-noise ratio (SNR) of resonance output by means of twice integral transform compared with the traditional SR method. Morlet wavelet transform can obtain finer frequency partitions and overcome the frequency aliasing compared with the classical wavelet transform. Therefore, through Morlet wavelet transform, the noise intensity of different scales can be adjusted to realize the resonance detection of weak periodic signal whatever it is a low-frequency signal or high-frequency signal. Thus the method is well-suited for enhancement of weak fault identification, whose effectiveness has been verified by the practical vibration signals carrying fault information. Finally, the proposed method has been applied to extract feature of the looseness fault of shaft coupling of wind turbine successfully.

Suggested Citation

  • Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
  • Handle: RePEc:eee:renene:v:60:y:2013:i:c:p:7-19
    DOI: 10.1016/j.renene.2013.04.005
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    References listed on IDEAS

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    1. 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.
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    5. An, Xueli & Jiang, Dongxiang & Li, Shaohua & Zhao, Minghao, 2011. "Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine," Energy, Elsevier, vol. 36(9), pages 5508-5520.
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    Cited by:

    1. Chen, Jinglong & Pan, Jun & Zhang, Chunlin & Luo, Xiaoyu & Zhou, Zitong & Wang, Biao, 2017. "Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification," Renewable Energy, Elsevier, vol. 103(C), pages 448-467.
    2. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    3. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    4. 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.
    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.

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