• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.27 no.4
• /
• pp.21-26
• /
• 2019

The purpose of this study is to suggest a method for the efficient preventive maintenance of aircraft gas turbine engine turbine blades. For this study, the types and characteristics of gas turbine engines and its turbine blades were studied, the turbine blade defect types that caused an In-Flight Shut Down(IFSD) were analyzed, the blade failure rate according to the blade life cycle was analyzed through the Weibull distribution, one of the statistical techniques. Through these research results, it is possible to supplement the problems of the life cycle management and maintenance method of the turbine blade, and to suggest the measures to strengthen the preventive maintenance of the turbine blade. In this analysis, when total cycle of turbine blade exceeds 18,000 cycles, the failure rate is over 98%, and then the special management measures are required.

• New & Renewable Energy
• /
• v.6 no.2
• /
• pp.5-11
• /
• 2010

This paper reviews three commercial softwares for wind climate data analysis in wind resource assessment; WAsP/Observed Wind Climate, WindRose and Windographer. Windographer is evaluated as the best software because of its variety of input data format, analysis functions, easiness of user interface, etc. For a quantitative understanding of uncertainty depending on software selection, a benchmark is carried out with the met-mast observation dataset at the Gimnyeong Wind Turbine Performance Test Site. It is found that Weibull parameter calculation and air density correction have a dependency on the software so that such uncertainty should be considered when an analysis software is selected. It is confirmed that annual energy production calculated by WAsP using a statistical table of frequency of occurrence may have some error compared to a time-series calculation method used by the other softwares.

• Journal of the Korean Ceramic Society
• /
• v.27 no.3
• /
• pp.321-328
• /
• 1990

For the reliability analysis of Sin/Hip silicon nitride, such as Weibull modulus m, scale parameter $\sigma$0, and Batdorf crack density coefficient kB were obtained by 4-point MOR test. And its theoretical failure probabilities under arbitrary stress state were predicted using finite element analysis and KARA II reliability analysis program, which was programmed for both surface adn volume flaws. For the verification of this theoretical results, the experimental failure probabilities were measured using ring-to-ring tests at room temperature as well as 4-point MOR tests at 100$0^{\circ}C$, and were compared with theoretical failure probabilities.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.9
• /
• pp.1109-1116
• /
• 2013

This study aims to investigate the spatial randomness of fatigue crack growth rate for the friction stir welded (FSWed) 7075-T651 aluminum alloy joints. Our previous fatigue crack growth test data are adopted in this investigation. To clearly understand the spatial randomness of fatigue crack growth rate, fatigue crack growth tests were conducted under constant stress intensity factor range (SIFR) control testing. The experimental data were analyzed for two different materials-base metal (BM) and weld metal (WM)-to investigate the effects of spatial randomness of fatigue crack growth rate and material properties, the friction stir welded (FSWed) 7075-T651 aluminum alloy joints, namely weld metal (WM) and base metal (BM). The results showed that the variability, as evaluated by Weibull statistical analysis, of the WM is higher than that of the BM.

• Smart Structures and Systems
• /
• v.21 no.5
• /
• pp.591-600
• /
• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.1-12
• /
• 2017

Many stochastic processes satisfy the Markov property exactly or at least approximately. An interested property in the Markov process is the first passage time. Since the sequential analysis by Wald, the approximation of the first passage time has been studied extensively. The Statistical computing technique due to the development of high-speed computers made it possible to calculate the values of the properties close to the true ones. This article introduces an exponentially weighted moving average (EWMA) control chart as an example of the Markov process, and studied how to calculate the average run length with problematic issues that should be cautioned for correct calculation. The results derived for approximation of the first passage time in this research can be applied to any of the Markov processes. Especially the approximation of the continuous time Markov process to the discrete time Markov chain is useful for the studies of the properties of the stochastic process and makes computational approaches easy.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2A
• /
• pp.197-205
• /
• 2008

In order to estimate mean wind speed in the Gwangyang-Myodo Region, the probability distribution model of extreme values has been used in the statistical analysis of joint distribution probability of daily maximum wind speed and corresponding direction in this paper. For this purpose frequency of daily maximum records at respective stations is inquired into and sample of largest yearly wind speed of sixteen compass direction and non-direction is extracted from daily data of maximum wind speed and appropriate direction of the meteorological observing stations nearby the bridge construction site. These extreme speed records are applied to Gumbel and Weibull distribution model and parameters are estimated through method of moment and method of least squares etc. And also, distribution and parameters are inquired into whether it is fitted through the probability plot correlation coefficient examination. From fitted parameters the largest yearly wind speed of sixteen compass direction and non-direction is extrapolated taking into account factors regarding sample size of data and distance from the bridge construction site according to the appropriate stations.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.9
• /
• pp.4049-4054
• /
• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• Proceedings of the Korean Society for Rock Mechanics Conference
• /
• 2011.09a
• /
• pp.89-97
• /
• 2011

Brittle failure of rock is a classical rock mechanics problem. Rock failure not only involves initiation and propagation of single crack, but also is a complex problem associated with initiation, propagation and coalescence of many cracks. As the most important feature of rock material properties is the heterogeneity, the Weibull statistical distribution is employed in the rock failure process analysis (RFPA) method to describe the heterogeneity in rock properties. In this paper, the applications of the RFPA method in geotechnical engineering and rockburst modeling are introduced with emphasis, which can provide some references for relevant researches.

• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.425-445
• /
• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.