Flapping wing aircrafts have demonstrated unique advantages in military and civil fields due to their bio-inspired flight mechanisms. However, non-uniform wear in driving mechanisms remains a critical reliability concern during prolonged operation. This study presents a stochastic wear prediction framework that systematically integrates joint clearance dynamics, contact force variations, and material interaction parameters. Through accelerated life testing with flight condition simulations, the method establishes quantitative correlations between multi-source variables and wear progression patterns. Experimental validation confirms the framework’s effectiveness in predicting asymmetric wear distribution, with comparative analysis showing significant improvements in prediction accuracy over conventional single-factor models. The results identify three dominant wear contributors: dynamic clearance fluctuations, impact force randomness, and material compatibility limitations. These findings directly support the development of adaptive lubrication systems and wear-resistant material selection guidelines, offering practical solutions for enhancing flapping wing aircrafts’ reliability in complex operational scenarios.

The reliability of the flapping-wing vehicle driving mechanism is jointly affected by the wear of its multiple hinges, and correlations exist between these wear processes. To accurately assess its reliability, this paper proposes an integrated modeling method that combines the Wiener process and Copula functions. Firstly, a Wiener process with time-scale transformation is employed to describe the stochastic wear evolution of each individual hinge. Subsequently, Copula functions are utilized to characterize the correlation structure among the wear increments of multiple hinges. Finally, the time-varying reliability of the system is calculated by integrating Monte Carlo simulation with the mechanism’s kinematic model. A case study on a specific flapping-wing driving mechanism demonstrates: a strong positive correlation exists between hinge wear processes; compared to the traditional assumption of independent wear, neglecting this correlation leads to an overestimation of the reliability assessment result by approximately 17.2%. This method provides a new approach for the reliability assessment of mechanical systems considering multiple correlated failure modes.