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Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear

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  • Liu, Xianzeng
  • Yang, Yuhu
  • Zhang, Jun

Abstract

Planetary gear trains equipped in wind turbine often run under slow speed and non-stationary load condition. The incipient gear faults in a wind turbine gearbox can hardly be detected yet might cause tremendous loss. In order to detect the incipient faults, a resultant vibration signal model is proposed to characterize the faulty features of a single stage planetary gear train working under non-stationary load conditions. For this purpose, an analytical dynamic model is developed. By introducing the crack-induced mesh stiffness and varying load into the dynamic model, the vibration responses of the system are predicted. Based on this, a resultant vibration signal model is developed in the form of weighted summation of mesh vibration signals. With the resultant model, the vibration signals of an example system are simulated and analyzed. The simulation results indicate that varying load and tooth crack make the system's vibration signals become extremely complicated in both time and frequency domains. The incipient tooth crack induced impulse vibration signals are too weak to be identified in the time domain but can be detected from the order spectrum. The simulation results from the resultant signal model are verified by the test rig experimental measurements.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:122:y:2018:i:c:p:65-79
    DOI: 10.1016/j.renene.2018.01.072
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    Cited by:

    1. 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.
    2. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network," Journal of Risk and Reliability, , vol. 234(1), pages 168-182, February.
    3. Rishi Kumar & Sankar Kumar Roy, 2022. "Model based diagnostic tool for detection of gear tooth crack in a wind turbine gearbox under constant load," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1666-1687, August.
    4. Wang, Cheng, 2024. "Study on dynamic performance and optimal design for differential gear train in wind turbine gearbox," Renewable Energy, Elsevier, vol. 221(C).
    5. Zhijian Wang & Likang Zheng & Wenhua Du & Wenan Cai & Jie Zhou & Jingtai Wang & Xiaofeng Han & Gaofeng He, 2019. "A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network," Complexity, Hindawi, vol. 2019, pages 1-17, June.
    6. 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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