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 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.

Fatigue Reliability Analysis System for Key Components of Aero-Engine

“…The thermal stress is caused by the nonuniform temperature field and temperature change. The temperature field distribution of the turbine blade can be expressed according to the quadratic curve [24] as follows:…”

Structural dynamic reliability analysis: review and prospects

“…stress, deformation, strain and so forth) is determined by multiple dynamic deterministic analyses based on the material property, structure loads and dimensions, and the samples of input variables and output responses are obtained. Then the limit state function is approximated by the response surface method (RSM) (Lu et al, 2020;Zhang et al, 2017;Kaymaz and McMahon, 2005), support vector regression (SVM) (Feng et al, 2019;Chen et al, 2022;Hariri-Ardebili and Pourkamali-Anaraki, 2018;Keshtegar et al, 2021), artificial neural network (ANN) (Li et al, 2021;Cherid et al, 2021;Peng et al, 2019) and Kriging (Teng et al, 2022;Zhang et al, 2021a, b;Jiang et al, 2019a, b) surrogate model, and the reliability is analyzed by combining the allowable value. Zhang and Bai (2012) presented the extremum RSM used for the reliability analysis of a two-link flexible robot manipulator.…”