Rolling bearings, as fundamental mechanical components, may develop surface defects during operation, leading to excessive vibration and compromised equipment performance. To address this issue, this study establishes a reliability and local sensitivity analysis framework for defective bearings. Specifically, an improved spherical defect profile model is developed by incorporating time-varying displacement excitation and contact stiffness excitation. A dynamic model for defective bearings is subsequently validated using the Case Western Reserve University (CWRU) bearing dataset. For enhanced computational efficiency, we propose a reliability and local sensitivity assessment method based on Adaptive Kriging-Monte Carlo Simulation with Dynamic Resampling Strategy (DRS-AK-MCS). Results demonstrate that under varying input dimensions, the DRS-enhanced Kriging model converges within 65, 72, and 78 iterations, achieving errors of 0.0574 %, 0.0471 %, and 0.0018 % respectively. This confirms the method’s effectiveness and robustness. Application of the framework reveals failure probability evolution patterns relative to defect sizes and identifies critical rankings of influencing factors. The proposed approach provides theoretical foundations for bearing optimization design, demonstrating balanced computational accuracy and efficiency with significant engineering applicability.

Failure of an aircraft Drag Chute Lock Mechanism (DCLM) can prevent the drag chute from being thrown properly, which may threaten the safety of aircraft and crew. The DCLM is affected by Multiple Progressive Damage (MPD) factors during operation, and its global sensitivity analysis introduces many parameters that substantially increase the computational cost. This paper proposes a global sensitivity analysis method based on partial derivatives to solve the above problems, which reduces the dimensionality of the parameters to decrease the computational cost and improve the efficiency of global sensitivity analysis. Based on the operating principle of DCLM, it is analyzed for MPD factors, including wear, impact and creep. A reliability model considering MPD factors is developed for the DCLM, which is analyzed using a global sensitivity calculation method that is based on partial derivatives, and optimization suggestions for improving reliability are given for important input parameters. The results of the numerical study show that the proposed partial-derivative-based global sensitivity analysis method improves the efficiency of sensitivity analysis under consideration of the MPD and increases the reliability of the mechanism through parameter optimization.