• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.6
• /
• pp.100-107
• /
• 1999

This paper describes a probabilistic remaining life assessment program for the creep crack growth. The probabilistic life assessment program is developed to increase the reliability of life assessment. The probabilistic life assessment involves some uncertainties, such as, initial crack size, material properties, and loading condition, and a triangle distribution function is used for random variable generation. The resulting information provides the engineer with an assessment of the probability of structural failure as a function of operating time given the uncertainties in the input data. This study forms basis of the probabilistic life assessment technique and will be extended to other damage mechanisms.

• Journal of Korean Society of Steel Construction
• /
• v.10 no.1 s.34
• /
• pp.37-45
• /
• 1998

The relational database system on fatigue strength was constructed, and the properties of fatigue life distribution were examined to analyze reliability and safety of metallic materials. Data manipulations were efficiently performed in relational fatigue strength database system using dependency diagram. Regardless of the distribution of fatigue strength, the proposed method, the Robust method and the complementary error function method using probability distribution, successfully estimated parameters of the 3-parameter Weibull distribution. The proposed criterion for estimating non-failure probability showed good results regardless of censoring time. The fatigue life distribution function described as a function of parameters of the Weibull distribution and applied stress ratio produced P-S-N characteristics reasonably.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.3
• /
• pp.393-397
• /
• 2006

The study about Accelerated Life Test and analysis of failed data is increased in order to predict and evaluate reliability of products, according as the development cycle of products is reduced. Therefore, the decision of optimal distribution function about failed data for accurate analysis of failed data and test condition for Accelerated Life Test is very important. This paper compares Anderson-Darling method with Likelihood Function method for the decision of optimal distribution function about failed data. Anderson-Darling considers only failed data and Likelihood Function considers both failed data and life-stress relationship in decision of distribution function. In the results of comparison about two methods, we found that the distribution function chosen by each method is different and the life time predicted by each decided distribution function is different.

• International Journal of Reliability and Applications
• /
• v.15 no.2
• /
• pp.125-150
• /
• 2014

Accelerated life tests (ALTs) are extensively used to determine the reliability of a product in a short period of time. Test units are subject to elevated stresses which yield quick failures. ALT can be carried out using constant-stress, step-stress, progressive-stress, cyclic-stress or random-stress loading and their various combinations. An ALT with linearly increasing stress is ramp-stress test. Much of the previous work on planning ALTs has focused on constant-stress, step-stress, ramp-stress schemes and their various combinations where the stress is generally increased. This paper presents an optimal design of ramp soak-stress ALT model which is based on the principle of Thermal cycling. Thermal cycling involves applying high and low temperatures repeatedly over time. The optimal plan consists in finding out relevant experimental variables, namely, stress rates and stress rate change points, by minimizing variance of reliability function with pre-specified mission time under normal operating conditions. The Burr type XII life distribution and time-censored data have been used for the purpose. Burr type XII life distribution has been found appropriate for accelerated life testing experiments. The method developed has been explained using a numerical example and sensitivity analysis carried out.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.4
• /
• pp.875-888
• /
• 2017

The Rayleigh distribution has been commonly used in life time testing studies of the probability of surviving until mission time. We focus on a reliability function of the Rayleigh distribution and deal with prior distribution on R(t). This paper is an effort to obtain Bayes estimators of rayleigh distribution with three different prior distribution on the reliability function; a noninformative prior, uniform prior and inverse gamma prior. We have found the Bayes estimator and predictive density function of a future observation y with each prior distribution. We compare the performance of the Bayes estimators under different sample size and in simulation study. We also derive the most plausible region, prediction intervals for a future observation.

• International Journal of Reliability and Applications
• /
• v.7 no.1
• /
• pp.13-25
• /
• 2006

A new class of life distributions is studied. This class is defined based on comparing the residual life time to the whole life in the moment generating function order giving 'the new better than used in the moment generating function order ageing class $(NBU_{mgf})$'. Fundamental properties of this class are given including some closure properties and characterizations. Finally, we consider new results about comparisons of age and block replacement policies when the underlying distribution belongs to $NBU_{mgf}$ aging classes.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.16 no.4
• /
• pp.33-41
• /
• 2012

The aim of the study is to investigate the method to estimate a shelf life of KM6 single base propellant by stochastic gamma process model. The state failure level is assumed that the degradation content of stabilizer is below 0.8%. The constant of time dependent shape function and the scale parameter of stationary gamma process are estimated by moment method. The state distribution at each storage time can be shown from probability density function of deterioration. It is estimated that the $B_{10}$ life, a time at which the cumulative failure probability is 10%, is 25 years and the $B_{50}$ life is 36 years from cumulative failure distribution function curve. The $B_{50}$ life can be treated as the average shelf life from the practical viewpoint and the lifetime can be expressed as distribution curve by using stochastic process theory.

• Journal of Korean Society of Steel Construction
• /
• v.14 no.2
• /
• pp.365-373
• /
• 2002

In this paper, the system reliability concept was presented to predict the lifespan of bridges. Lifetime distribution functions (survivor functions) were used to model real bridges to predict their remaining life. Using the system reliability concept and lifetime distribution functions (survivor functions), a program called LIFETIME was developed. The survivor functions give the reliability of component at time t. The program was applied to an existing Colorado state highway bridge to predict the failure probability of the time-dependent system. The bridge was modeled as a system, with failure probability computed using time-dependent deteriorating models.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.6
• /
• pp.67-72
• /
• 2020

Life tests are essential in reducing the catastrophic damage caused by the accidents of large machinery such as aircraft and ships. However, life tests are challenging to implement due to the high costs and time required to test the life of large machinery parts. Therefore, it is advantageous and convenient to apply accelerated life test techniques for key components to reduce costs and time. In fact, extensive research has already been conducted on these techniques. However, recently, there have been cases in which an experimental value was applied to the shape parameter of the Weibull distribution used in the reliability test, but the test time was not significantly reduced. Therefore, in this paper, the shape parameters are estimated from the probability density function of the Weibull distribution for the analysis of an accelerated life test for bladder accumulators, which are core components of military helicopters. The test time was derived based on the number of samples and confidence level by substituting it into the test time equation. Next, the accelerated life test time was calculated using the steady-state test time with an acceleration factor obtained from the Arrhenius model. The steady-state life test required approximately 15,000 H, whereas the accelerated life test time for one sample at 100 ℃ was 34% shorter than that of the steady-state life test.

• Journal of Information Technology Applications and Management
• /
• v.24 no.2
• /
• pp.71-80
• /
• 2017

Software reliability is one of the most elementary and important problems in software development In order to find the software failure occurrence, the instantaneous failure rate function in the Poisson process can have a constant, incremental or decreasing tendency independently of the failure time. In this study, we compared the reliability performance of the software reliability model using the parameters of Pareto life distribution with the intensity decreasing pattern and the shape parameter of Erlang life distribution with the intensity increasing and decreasing pattern in the software product testing. In order to identify the software failure environment, the parametric estimation was applied to the maximum likelihood estimation method. Therefore, in this paper, we compare and evaluate software reliability by applying software failure time data. The reliability of the Erlang and Pareto life models is shown to be higher than that of the Pareto lifetime distribution model when the shape parameter is higher and the Erlang model is more reliable when the shape parameter is higher. Through this study, the software design department will be able to help the software design by applying various life distribution and shape parameters, and providing basic knowledge using software failure analysis.