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<p>Aiming to evaluate the dynamic reliability of gear transmission systems, this study develops a reliability model that incorporates multiple failure modes, including root bending, tooth surface contact, and tooth surface wear. The copula function is used to capture the dependencies between these failure modes. The maximum stress or minimum strength of the gear is determined using order statistics, while the Gamma process models strength degradation, accounting for random impacts following a Poisson distribution. Dynamic reliability for root bending and tooth surface contact failures is assessed using the stress–strength interference model, while the cumulative wear-threshold interference model is applied to tooth surface wear. The proposed model is validated through a case study of a high-speed train gear pair. Results offer valuable insights for optimizing gear design and predictive maintenance, thereby enhancing the reliability and operational efficiency of gear transmission systems.</p>

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Dynamic reliability modeling of gear transmission system considering multiple failure modes based on copula functions

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Shuzhi Gao,
  • Jiawei Li,
  • Yimin Zhang,
  • Tiejun Li

摘要

Aiming to evaluate the dynamic reliability of gear transmission systems, this study develops a reliability model that incorporates multiple failure modes, including root bending, tooth surface contact, and tooth surface wear. The copula function is used to capture the dependencies between these failure modes. The maximum stress or minimum strength of the gear is determined using order statistics, while the Gamma process models strength degradation, accounting for random impacts following a Poisson distribution. Dynamic reliability for root bending and tooth surface contact failures is assessed using the stress–strength interference model, while the cumulative wear-threshold interference model is applied to tooth surface wear. The proposed model is validated through a case study of a high-speed train gear pair. Results offer valuable insights for optimizing gear design and predictive maintenance, thereby enhancing the reliability and operational efficiency of gear transmission systems.