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Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform

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  • Teng, Wei
  • Ding, Xian
  • Cheng, Hao
  • Han, Chen
  • Liu, Yibing
  • Mu, Haihua

Abstract

Fault diagnosis of a wind turbine gearbox can schedule maintenance strategy on a wind turbine and save operational cost for wind farms. Owing to low rotational speed and weak vibration energy, fault diagnosis for planetary stage is arduous in a wind turbine gearbox involving multi-stage transmissions. A novel modulation model is presented to support the fault diagnosis of the planetary stage of a wind turbine gearbox, which is described as the mesh frequency of intermediate stage, high speed stage, or mechanical natural frequency of the gearbox is a carrier wave modulated by the mesh frequency of planetary stage with distributed faults. Even possessing this presented vibration model, the fault feature of planetary stage with lower rotational speed is easily concealed by the meshing vibration energy of ordinary stages with higher speed, especially when faults simultaneously arise in the ordinary stages. Aiming at the diagnosis of the above compound faults in an industrial wind turbine gearbox, empirical wavelet transform is utilized to adaptively find weak fault frequency in planetary stage as well as evident fault characteristics in other ordinary stages.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:136:y:2019:i:c:p:393-402
    DOI: 10.1016/j.renene.2018.12.094
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    6. 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.
    7. 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.
    8. Chang, Yuanhong & Chen, Jinglong & Qu, Cheng & Pan, Tongyang, 2020. "Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels," Renewable Energy, Elsevier, vol. 153(C), pages 205-213.
    9. Stefan Jonas & Dimitrios Anagnostos & Bernhard Brodbeck & Angela Meyer, 2023. "Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering," Energies, MDPI, vol. 16(4), pages 1-16, February.
    10. 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.
    11. 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.
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