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-depe...
Highlights
Reliability is defined as the probability that a system/ component performs its intended function successfully for a given period of time, under stated conditions.[1]
Since the mechanical systems become more and more complex, multi-failure modes occurring and time-consuming design of experiments (DOE), instantaneous reliability estimation techniques by data-driven technology combining with physics of failure modeling methods have been developed and widely used in aerospace, transportation, oil and gas exploration, and petrochemical industries.[8,9,10,11,12]
For a highly nonlinear dynamic system, it is difficult to obtain the probability density function (PDF) of the extreme values gmax(x, d, y(t), t) with complex uncertainties, design of experiment combining surrogate modeling methods can be used as an alternative way to approximately estimate the probability of failure during a given time interval
Summary
Reliability is defined as the probability that a system/ component performs its intended function successfully for a given period of time, under stated conditions.[1]. For a highly nonlinear dynamic system, it is difficult to obtain the PDF of the extreme values gmax(x, d, y(t), t) with complex uncertainties, design of experiment combining surrogate modeling methods can be used as an alternative way to approximately estimate the probability of failure during a given time interval. A time-dependent reliability method based on surrogate modeling and data clustering algorithm is proposed. (1) Initial time-dependent surrogate modeling using BP neural network; (2) Most probable failure domains identification using data clustering; (3) Extreme values searching using Genetic Algorithm, and surrogate modeling for extreme values gmax(x, y(t)). The contour of the initial surrogate model is plotted, which shows that the surrogate model is accurate in the domain where the random variables have high probability density and near the limit state function This characteristic can reduce training points and improve modeling accuracy. Clustering the 1000 candidate points, calculate the joint probability density (JPD) values and response of these candidate points, using
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