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.