• Proceedings of the Korean Reliability Society Conference
• /
• 2000.04a
• /
• pp.213-222
• /
• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• International Journal of Reliability and Applications
• /
• v.4 no.1
• /
• pp.1-12
• /
• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.

• Geomechanics and Engineering
• /
• v.12 no.2
• /
• pp.269-294
• /
• 2017

This study aimed to realize the creation of fuzzy stochastic damage to describe reliability more essentially with the analysis of harmony of damage conception, probability and fuzzy degree of membership in interval [0,1]. Two kinds of fuzzy behaviors of damage development were deduced. Fuzzy stochastic damage models were established based on the fuzzy memberships functional and equivalent normalization theory. Fuzzy stochastic damage finite element method was developed as the approach to reliability simulation. The three-dimensional fuzzy stochastic damage mechanical behaviors of Jianshan mine slope were analyzed and examined based on this approach. The comprehensive results, including the displacement, stress, damage and their stochastic characteristics, indicate consistently that the failure foci of Jianshan mine slope are the slope-cutting areas where, with the maximal failure probability 40%, the hazardous Domino effects will motivate the neighboring rock bodies' sliding activities.

• International Journal of Reliability and Applications
• /
• v.4 no.1
• /
• pp.27-40
• /
• 2003

Maintenance models involving minimal imperfect repair frequently appear in the literature of reliability and operations research. Most of the literatures concerning the stochastic behavior of repairable systems assume that it takes negligible time to repair a failed system and so the length of repair time does not affect the maintenance strategy. It is more realistic to consider the length of repair times in developing maintenance model, however. In this paper, we consider an imperfect repair model with random repair time and investigate some stochastic properties of the number of perfect repairs and the number of minimal repairs. Also we derive the expressions for evaluating the expected numbers of perfect and minimal repairs in general and apply these formulas for certain parametric life distributions.

• Journal of the Korean Society for Railway
• /
• v.11 no.1
• /
• pp.75-80
• /
• 2008

Human factors still play a significant part in many railway accidents. It is well blown that SPAD (Signal Passed at Danger) remains as the single most cause of railway accidents. In this study a stochastic model is developed to quantify SPAD human reliability. This model provides closed-form mathematical expressions into which multiple factors affecting the reliability of man-machine systems can be incorporated. Two basic elements are combined to form the framework for modeling: random signal occurrence and transient human performance characteristics.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.4
• /
• pp.1-9
• /
• 2012

Decisions on reliability screening rules and burn-in policies are determined based on the estimated reliability. The variability in a semiconductor manufacturing process does not only causes quality problems but it also makes reliability estimation more complicated. This study investigates the nonuniformity characteristics of integrated circuit reliability according to defect density distribution within a wafer and between wafers then develops optimal burn-in policy based on the estimated reliability. New reliability estimation model based on yield information is developed using a spatial stochastic process. Spatial defect density variation is reflected in the reliability estimation, and the defect densities of each die location are considered as input variables of the burn-in optimization. Reliability screening and optimal burn-in policy subject to the burn-in cost minimization is examined, and numerical experiments are conducted.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.10a
• /
• pp.351-358
• /
• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• Structural Engineering and Mechanics
• /
• v.86 no.3
• /
• pp.349-359
• /
• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.703-712
• /
• 2017

The composite blades of offshore wind turbines accumulate structural damage such as fatigue cracking due to harsh operation environments during their service time, leading to premature structural failures. This paper investigates various fatigue crack models for reproducing crack development in composite blades and proposes a stochastic approach to predict fatigue crack evolution and to analyse failure probability for the composite blades. Three typical fatigue models for the propagation of fatigue cracks, i.e., Miner model, Paris model and Reifsnider model, are discussed to reproduce the fatigue crack evolution in composite blades subjected to cyclical loadings. The lifetime probability of fatigue failure of the composite blades is estimated by stochastic deterioration modelling such as gamma process. Based on time-dependent reliability analysis and lifecycle cost analysis, an optimised maintenance policy is determined to make the optimal decision for the composite blades during the service time. A numerical example is employed to investigate the effectiveness of predicting fatigue crack growth, estimating the probability of fatigue failure and evaluating an optimal maintenance policy. The results from the numerical study show that the stochastic gamma process together with the proper fatigue models can provide a useful tool for remaining useful life predictions and optimum maintenance strategies of the composite blades of offshore wind turbines.

• International Journal of Reliability and Applications
• /
• v.9 no.2
• /
• pp.167-182
• /
• 2008

The objective of this paper is to discuss the stochastic analysis and the statistical inference of a three-states semi-Markov reliability model. Using the maximum likelihood procedure, the parameters included in this model are estimated. Based on the assumption that the lifetime and repair time of the system are gener-alized Weibull random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is derived. Many important special cases are discussed. Finally, the obtained results are compared with those available in the literature.