Simulation-based uncertainty correlation modeling in reliability analysis

Abstract

Due to destructive effects like temperature and radiation, today's embedded systems have to deal with unreliable components. The intensity of these effects depends on uncertain aspects like environmental or usage conditions such that highly safety-critical systems are pessimistically designed for worst-case mission profiles. These uncertain aspects may affect several components simultaneously, implying correlation across uncertainties in their reliability. This paper enables a state-of-the-art uncertainty-aware reliability analysis technique to consider multiple arbitrary correlations; in other words, components' reliability is affected by several uncertain aspects to different degrees. This analysis technique combines reliability models such as binary decision diagrams with a Monte Carlo simulation, and derives the uncertainty distribution of the system's reliability with insights on the mean, quantile intervals, and so on. The proposed correlation method aims at generating correlated samples from the uncertainty distribution of components' reliability such that the shape and statistical properties of each individual distribution remain unchanged. Experimental results confirm that the proposed correlation model enables the employed uncertainty-aware analysis to accurately calculate uncertainty at system level.

Uncertainty-Aware Compositional System-Level Reliability Analysis

“…Take temperature as an example: Components that are fabricated in the same package may be exposed to the same temperature, which means their reliability characteristics can be considered in a correlation group, whereas components in different packages might be considered independent. Assuming that the uncertainty sources and the correlation groups are given, we introduce models for obtaining correlated samples from the uncertainty distribution of component reliability functions in [29,31]: To sample from an uncertain parameter p, we check if it is a member of any correlation group or not. If p is a member of G, we first generate a random probability g for the group G at the beginning of each implementation evaluation step and then calculate a sample from p using the inverse CDF!…”

Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

“…The SILK surrogate model method removed the estimation of the performance extreme value in the inner loop and directly constructed the surrogate model for the performance function, thus this method is more efficient than those of the double-loop surrogate model methods. Other reliability analysis methods can be found in Khosravi et al, 20 Harnpornchai, 21 Yang et al 22 and Chen et al 23 Although the SILK surrogate model method 19 for estimating the TDFP is accurate and efficient, the procedure of constructing the surrogate model is generally time-demanding, especially for the small TDFP case. It is supposed that the number of the input variable samples and the discretized time instants are N and N t , respectively, in the SILK surrogate model method, thus this method needs to call the current constructed surrogate model N 3 N t times to identify the new training sample points in each iterative step.…”

“…The SILK surrogate model method removed the estimation of the performance extreme value in the inner loop and directly constructed the surrogate model for the performance function, thus this method is more efficient than those of the double-loop surrogate model methods. Other reliability analysis methods can be found in Khosravi et al, 20 Harnpornchai, 21 Yang et al 22 and Chen et al 23…”

Dynamic reliability analysis for structure with temporal and spatial multi-parameter

“…2 Time-independent reliability analysis concerns the time-dependent reliability analysis at each time instant. At present, many time-independent reliability analysis techniques have been proposed, such as Monte Carlo simulation (MCS), 3 first-order reliability method (FORM), 4 high-order reliability method 5 and others. 6–8…”

“…Time-dependent reliability analysis also called dynamic reliability analysis quantifies the probability that a structure or system survives after it has worked for a certain time t or over the period ½t, t. 2 Time-independent reliability analysis concerns the time-dependent reliability analysis at each time instant. At present, many time-independent reliability analysis techniques have been proposed, such as Monte Carlo simulation (MCS), 3 first-order reliability method (FORM), 4 highorder reliability method 5 and others. [6][7][8] Nowadays, three mainstream methods are widely used to estimate the dynamic reliability, that is, the first-passage-based methods, [9][10][11][12][13][14][15][16] the extreme performance-based methods [17][18][19][20] and the sampling-based methods.…”