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Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear

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  • Kong, Yun
  • Wang, Tianyang
  • Chu, Fulei

Abstract

Condition monitoring and fault diagnosis for wind turbine gearbox is significant to save operation and maintenance costs. However, strong interferences from high-speed parallel gears and background noises make fault detection of wind turbine planetary gearbox challenging. This paper addresses the fault diagnosis for wind turbine planetary ring gear, which is intractable for traditional spectral analysis techniques, since the fault characteristic frequency of planetary ring gear can be resulted from the revolving planet gears inducing modulations even in healthy conditions. The main contribution is to establish an adaptive empirical wavelet transform framework for fault-related mode extraction, which incorporates a novel meshing frequency modulation phenomenon to enhance the planetary gear related vibration components in wind turbine gearbox. Moreover, an adaptive Fourier spectrum segmentation scheme using iterative backward-forward search algorithm is developed to achieve adaptive empirical wavelet transform for fault-related mode extraction. Finally, fault features are identified from envelope spectrums of the extracted modes. The simulation and experimental results show the effectiveness of the proposed framework for fault diagnosis of wind turbine planetary ring gear. Comparative studies prove its superiority to reveal evident fault features and avoid the ambiguity from the planet carrier rotational frequency over ensemble empirical mode decomposition and spectral kurtosis.

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  • Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:1373-1388
    DOI: 10.1016/j.renene.2018.09.027
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    Cited by:

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    2. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    3. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
    4. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    5. Wu, Zhe & Zhang, Qiang & Cheng, Lifeng & Hou, Shuyong & Tan, Shengyue, 2020. "The VMTES: Application to the structural health monitoring and diagnosis of rotating machines," Renewable Energy, Elsevier, vol. 162(C), pages 2380-2396.
    6. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
    7. Mingzhu Tang & Qi Zhao & Steven X. Ding & Huawei Wu & Linlin Li & Wen Long & Bin Huang, 2020. "An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes," Energies, MDPI, vol. 13(4), pages 1-16, February.
    8. 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).
    9. Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.

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