Reliability Modeling of Wind Turbine Gearbox System Considering Failure Correlation Under Shock–Degradation

To address traditional methods' limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock-degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress-strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel-Clayton-Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox's planetary gear-stage system covers four scenarios: natural degradation, shock-degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model's effectiveness and alignment with real-world operational conditions.