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Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis

Author

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  • Yi, Cancan
  • Yu, Zhaohong
  • Lv, Yong
  • Xiao, Han

Abstract

Due to constant change of rotational speed and low frequency property of vibration signal, it is difficult to identify the weak time-varying fault features of direct-driven wind turbines during early period. In view of the problems of variable speed and strong time-varying characteristics, the traditional Time-Frequency (TF) analysis methods always generate a low TF resolution. Rearrangement Method (RM), as a mathematically excellent and efficient solution, is used to improve the TF energy localization, whereas it cannot reconstruct signals. To make TF plane more readable, RM can be utilized to make up the deficiency of the frequency-reassigned Synchrosqueezing Transform (SST). On such basis, this paper puts forward a novel TF analysis method, namely RFSST2, which has drawn on the advantages of RM and Second-Order Synchrosqueezing Transform based on STFT (FSST2). This method is used to sharpen the blurry TF energy for strong time-varying modulated signals. Besides, it can reconstruct the perfect signals. After the extraction the rotation speed curve from high-resolution TF plane generated by RFSST2, the order analysis without tachometer is performed to address the problem of variably rotating speed. To prove the proposed method is superior in processing complex multi-component signals, we respectively estimate the variable speed of wind turbines and deal with the fault signals from the gear fault simulation test-bed and from actual 1.5 MW direct-drive wind turbines. The analysis result obtained by comparing different TF methods shows that the method put forward by this paper can effectively extract time-varying fault characteristics under non-stationary condition.

Suggested Citation

  • Yi, Cancan & Yu, Zhaohong & Lv, Yong & Xiao, Han, 2020. "Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis," Renewable Energy, Elsevier, vol. 161(C), pages 736-749.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:736-749
    DOI: 10.1016/j.renene.2020.07.128
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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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    4. 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.
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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.
    3. 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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