Due to the complex uncertainty of working loads and design parameters, time-dependent reliability estimation is timeconsuming. Various works aim to improve the accuracy and efficiency of time-dependent reliability estimation methods with a known time-dependent response of the mechanical system. Time-dependent reliability calculation with complex uncertainty and unknown limit state function are more complex. In this article, surrogate modeling and data clustering technology are utilized to estimate the time-dependent reliability of mechanical structure. First, the physics of failure with respect to time for a mechanical structure is analyzed, and BP neural network is introduced to build the surrogate model of time-dependent response for mechanical structure. Second, data clustering technique is used to find the most probable failure domains. Furthermore, the Genetic Algorithm is utilized to search the extreme values of the response at the most probable failure points during the given time interval. Then, the surrogate model for the extreme values at the most probable failure points is approximately established using BP neural network and Monte Carlo simulation is used for time-dependent reliability estimation. Finally, two examples are presented to verify the accuracy and efficiency of the proposed method.

This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.