A time-dependent reliability estimation method based on surrogate modeling and data clustering

Abstract

Due to the complex uncertainty of working loads and design parameters, time-dependent reliability estimation is time-consuming. 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.