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Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings

Author

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  • Jianxiong Gao

    (School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China)

  • Yuanyuan Liu

    (School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China)

  • Yiping Yuan

    (School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China)

  • Fei Heng

    (School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China)

Abstract

A novel method is proposed to investigate the pattern of variation in the residual strength and reliability of wind turbine gear. First, the interaction between loads and the effect of the loading sequence is considered based on the fatigue damage accumulation theory, and a residual strength degradation model with few parameters is established. Experimental data from two materials are used to verify the predictive performance of the proposed model. Secondly, the modeling and simulation of the wind turbine gear is conducted to analyze the types of fatigue failures and obtain their fatigue life curves. Due to the randomness of the load on the gear, the rain flow counting method and the Goodman method are employed. Thirdly, considering the seasonal variation of load, the decreasing trend of gear fatigue strength under multistage random load is calculated. Finally, the dynamic failure rate and reliability of gear fatigue failure under multistage random loads are analyzed. The results demonstrate that the randomness of residual strength increases with increasing service time. The seasonality of load causes fluctuations in the reliability of gear, providing a new idea for evaluating the reliability of the wind turbine gear.

Suggested Citation

  • Jianxiong Gao & Yuanyuan Liu & Yiping Yuan & Fei Heng, 2023. "Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:4013-:d:1244966
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    References listed on IDEAS

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    1. Hongsheng Su & Yifan Zhao & Xueqian Wang, 2023. "Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    2. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    3. Altunkaynak, Abdüsselam & Erdik, Tarkan & Dabanlı, İsmail & Şen, Zekai, 2012. "Theoretical derivation of wind power probability distribution function and applications," Applied Energy, Elsevier, vol. 92(C), pages 809-814.
    4. Hongyan Dui & Xinyue Wang & Haohao Zhou, 2023. "Redundancy-Based Resilience Optimization of Multi-Component Systems," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    5. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    7. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
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    Cited by:

    1. Xiaocui Chen & Qirui Wang & Yuquan Zhang & Yuan Zheng, 2024. "Dynamic Behavior of a 10 MW Floating Wind Turbine Concrete Platform under Harsh Conditions," Mathematics, MDPI, vol. 12(3), pages 1-19, January.

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