Abstract
Accurate and efficient reliability evaluation is critical to ensure the safety of gear mechanism. This paper aims to develop an effective and practical method for time-dependent kinematic reliability of gear mechanism. Firstly, dynamic model of gear mechanism is established, and a surrogate model of kinematic error is obtained based on BP neural network. After that, we employ a sequential decoupling strategy of efficient global optimization to transform the time-dependent reliability problem into a time-independent one, with which the second-order information of the extreme limit-state function can be then obtained. Finally, the saddle-point approximation method is applied to estimate the time-dependent kinematic reliability of the gear mechanism. The accuracy and efficiency of the proposed method are verified by several engineering problems, and comparisons are made against other existing reliability methods. Results of the engineering cases show that the proposed method can effectively reduce the limit-state function call numbers while reaching the same accuracy as Monte Carlo Simulation.
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