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Planet gear fault localization for wind turbine gearbox using acoustic emission signals

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  • Zhang, Yu
  • Lu, Wenxiu
  • Chu, Fulei

Abstract

Acoustic emission (AE) techniques have been rapidly developed for use as powerful fault-diagnosis tools, especially in fault localization. Traditionally, AE signals were directly used for localization on simple models such as pipes and plates without subdivision; however, this effect is usually not ideal for complex structures. In this study, a new method capable of obtaining the precise time of arrival for AE signals is proposed to localize the faulty planet gear in a wind turbine gearbox; the core localization concept is to determine a valid physical parameter that presents the linear correlation with distance. Because compressive waves present a constant speed on any given frequency band, continuous wavelet transform based on the Morlet wavelet is used to extract those waves from other components of AE waves. Results obtained after applying this method to actual planet gear fault localization are satisfactory, indicating that this method offers several advantages to fault localization in complex structures.

Suggested Citation

  • Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    • Handle: RePEc:eee:renene:v:109:y:2017:i:c:p:449-460
    DOI: 10.1016/j.renene.2017.03.035
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    References listed on IDEAS

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    Cited by:

    1. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    2. 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.
    3. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    4. Sequeira, C. & Pacheco, A. & Galego, P. & Gorbeña, E., 2019. "Analysis of the efficiency of wind turbine gearboxes using the temperature variable," Renewable Energy, Elsevier, vol. 135(C), pages 465-472.
    5. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
    6. 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.
    7. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    8. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    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.
    10. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.

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