• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.346-352
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• 2009

In the present study, the Weibull statistical analysis using the Monte-Carlo simulation has been performed to investigate the micro-Vickers hardness measurement reliability considering the variability. Experimental indentation test were performed with a micro-Vickers hardness tester for as-received and quenching and tempering specimens in SCM440 steels. The distribution of micro-Vickers hardness is found to be 2-parameter Weibull distribution function. The mean values and coefficients of variation (COV) for both data set are compared with results based on Weibull statistical analysis. Finally, Monte-Carlo simulation was performed in order to evaluate the effect of sample size on the micro-Vickers hardness measurement reliability. For the parent distribution with shape parameter 30.0 and scale parameter 200.0 (COV=0.040), the number of sample data required to obtain the true Weibull parameters was founded by 20. For the parent distribution with shape parameter 10.0 and scale parameter 200.0 (COV=0.1240), the number of sample data required to obtain the true Weibull parameters was founded by 30.

• Journal of Applied Reliability
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• v.1 no.1
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• pp.17-33
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• 2001

The characteristics of Weibull distribution are investigated as a function of shape parameter. The statistical estimation methods of the shape parameter and statistical comparison methods of two or more shape parameters are studied. Assuming Weibull distribution, statistical analysis of bending-strength data of alumina titanium carbide ceramic matrix composites machined two different methods are performed.

• Journal of Power System Engineering
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• v.14 no.4
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• pp.56-62
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• 2010

This paper is concerned with the stochastic nature of elevated temperature tensile strength and creep rupture time in 18Cr-8Ni stainless steels. The Weibull statistical analysis using the NRIM data sheet has been performed to investigate the effects of variability of the elevated temperature tensile strength and creep rupture time on the testing temperature. From those investigations, the distributions of temperature tensile strength and creep rupture time were well followed in 2-parameter Weibull. The shape parameter and scale parameter for the Weibull distribution of tensile strength were decreased with increasing the testing temperature. For the creep rupture time, generally, the shape parameter were decreased with increasing the testing temperature.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1924-1934
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• 2023

In this study, the probabilistic fatigue life model for Ni-base alloys was developed based on the Weibull distribution using statistical analysis of fatigue data reported in NUREG/CR-6909 and the new fatigue data of Alloy 52M/152 and 82/182. The developed Weibull model can consider right-censored data (i.e., non-failed data) and quantify the improved safety (or reliability) based on the level of failure probability. The overall margin in the current fatigue design limit model (ASME design curve + NUREG/CR-6909 F_en model) is similar to that of the Weibull model with a cumulative failure probability of approximately 2.5%. The margin in the current fatigue design limit model demonstrated inconsistencies for the Ni-base alloy weld data, whereas the Weibull model showed a consistent margin. Therefore, the Weibull model can systematically mitigate the excessive safety margin.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.143-148
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• 2001

A dataset having missing observations is often completed by using imputed values. In this paper the performances and accuracy of complete case methods and four imputation procedures are evaluated when missing values exist only on the response variables in the Weibull regression model. Our simulation results show that compared to other imputation procedures, in particular, hotdeck and Weibull regression imputation procedure can be well used to compensate for missing data. In addition an illustrative real data is given.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.106-108
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• 1997

In this paper, the breakdown strength of Pure HDPE(High density polyethylene) films and aged HDPE films were evaluated using 2-parameter Weibull distribution function. The result show that both were fitted 2-parameter Weibull distribution. This method could be used to be the diagnostic tool to evalute the insulation performance and endurance under the multiple stresses.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.159-165
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• 2007

Using statistical methods a change-point analysis of oil supply disruption is conducted. The statistical distribution of oil supply disruption is a weibull distribution. The detection of the change-point is applied to Bayesian method and weibull parameters are estimated through Markov chain monte carlo and parameter approach. The statistical approaches to the estimation for the change-point and weibull parameters is implemented with the sets of simulated and real data with small sizes of samples.

• Journal of Power System Engineering
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• v.21 no.3
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• pp.37-44
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• 2017

In this study, failure analysis of the angular pin for molding machines to aluminum component molding was carried out. SM45C steel was used for the angular pin, it was surface hardened by the induction surface hardening heat treatment. The cross section of damaged angular pin was observed, and micro Vickers hardness value from the fractured part was measured. Brittle fracture was occurred from the fracture surface of angular pin, therefore, impact toughness value was evaluated by V-notch Charpy impact test. It was confirmed that the impact absorption energy was high when was tempered at a high temperature for a long time, and the toughness was slightly increased. Also, 2-parameter Weibull statistical analysis was investigated in order to evaluate the reliability of the measured micro Vickers hardness values and absorbed energy. The micro Vickers hardness and absorbed energy well followed a two-parameter Weibull probability distribution, respectively. The reverse design against angular pin was proposed as possible by using test results.

• Wind and Structures
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• v.31 no.5
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• pp.419-430
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• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.