• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.555-560
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• 2010

One-dimensional approach to two-dimensional warranty data involves modeling us- age as a function of time. Iskandar (1993) suggests a simple linear model for usage. However, simple linear form of intensity function is of limited value to model the situa-tion where the intensity varies over time. In this study Weibull intensity is considered where the scale parameter is expressed in terms of different models. We will nd out how each parameter in the model a ects the warranty cost and which model gives a bigger number of failures within the two-dimensional warranty region.

• International Journal of Reliability and Applications
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• 제14권2호
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• pp.107-114
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• 2013

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

• 한국해양공학회지
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• 제17권2호
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• pp.54-59
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• 2003

The characteristics of the parameters for the probability distribution of fatigue crack growth life, using the non-Gaussian random process simulation method is investigated. In this paper, the material resistance to fatigue crack growth is treated as a spatial random process, which varies randomly on the crack surface. Using the previous experimental data, the crack length equals the number of cycle curves that are simulated. The results are obtained for constant stress intensity factor range conditions with stress ratios of R=0.2, three specimen thickness of 6, 12 and 18mm, and the four stress intensity level. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Wiubull,, showing a slight dependence on specimen thickness and stress intensity level. The shape parameter, $\alpha$, does not show the dependency of thickness and stress intensity level, but the scale parameter, $\beta$, and location parameter, ${\gamma}$, are decreased by increasing the specimen thickness and stress intensity level. The slope for the stress intensity level is larger than the specimen thickness.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2002년도 추계학술대회 논문집
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• pp.301-306
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• 2002

The characteristics of parameters for the probability distribution of fatigue crack growth lives by the non-Gaussian random process simulation method is investigated. In this paper, the material resistance to fatigue crack growth is treated as a spatial random process, which varies randomly on the crack surface. Using the previous experimental data, the crack length - the number of cycles curves are simulated. The results are obtained for constant stress intensity factor range conditions with stress ratio of R=0.2, three specimen thickness of 6, 12 and 18mm, and the four stress intensity level. The probability distribution function of fatigue crack growth lives seems to follow the 3-parameter Wiubull and shows a slight dependence on specimen thickness and stress intensity level. The shape parameter, ${\alpha}$, does not show the dependency of thickness and stress intensity level, but the scale parameter, ${\beta}$, and location parameter, ${\upsilon}$, are decreased by increasing the specimen thickness and stress intensity level. The slope for the stress intensity level is larger than the specimen thickness.

• 대한기계학회논문집A
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• 제23권7호
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• pp.1139-1146
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• 1999

Reliability analysis of structures based on fracture mechanics requires knowledge on statistical characteristics of the parameter C and m in the fatigue crack growth law, $da/dN=C({\Delta}K)^m$. The purpose of the present study is to investigate if it is possible to predict fatigue crack growth rate by only the fluctuation of the parameter C. In this study, Paris-Erdogan law is adopted, where the author treat the parameter C as random and m as constant. The fluctuation of crack growth rate is assumed only due to the parameter C. The growth resistance coefficient of material to fatigue crack growth (Z=1/C) was treated as a spatial stochastic process, which varies randomly on the crack path. The theoretical crack growth rates at various stress intensity factor range are discussed. Constant ${\Delta}K$ fatigue crack growth tests were performed on the structural steel, SM45C. The experimental data were analyzed to determine the autocorrelation function and Weibull distributions of the fatigue crack growth resistance. And also, the effect of the parameter m of Paris' law due to variation of fatigue crack growth resistance was discussed.

• 대한기계학회논문집A
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• 제20권7호
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• pp.2159-2166
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• 1996

The stochastic properties of variation in fatigue crack growth are important in reliability and stability of structures. In this study,the stochastic model for the variation of fatigue crack growth rate was proposed in consideration of nonhomogeneity of materials. For this model, experiments were ocnducted on 7075-T6 aluminum alloy under the constant stress intensity factor range. The variation of fatigue crack growth rate was expressed by random variables Z and r based on the variation of material coefficients C and m in the paris-Erodogan's equation. The distribution of fatigue life with respect to the stress intensity factor range was evaluated by the stochastic Markov chain model based on the Paris-Erdogan's equation. The merit of proposed model is that only a small number of test are required to determine this this function, and fatigue crack growth life is easily predicted at the given stress intensity factor range.

• 한국해양공학회지
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• 제11권3호
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• pp.76-88
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• 1997

Constant .DELTA.K fatigue crack growth rate experiments were performed by applying an intermediate single and multiple overload for structural steel, SM45C. The purpose of the present study is to investigate the influence of multiple overloads at various stress intensity factor ranges and the effect of statistical variability of crack retardation behavior. The normalized delayed load cycle, delayed crack length and the minimum crack growth rate are increased with increasing baseline stress intensity factor range when the overload ratio and the number of overload application were constant. The crack retardation under low baseline stress intensity factor range increases by increasing the number of overload application, but the minimum crack growth rate decreases by increasing the number of overload application. A strong linear correlation exists between the minimum crack growth rate and the number of overload applications. And, it was observed that the variability in the crack growth retardation behavior are presented, the probability distribution functions of delayed load cycle, delayed crack length and crack growth life are 2-parameter Weibull. The coefficient of variation of delayed load cycle and delayed crack length for the number of 10 overload applications data are 14.8 and 9.2%, respectively.

• Smart Structures and Systems
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• 제21권5호
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• pp.591-600
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• 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.

• Journal of Mechanical Science and Technology
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• 제15권1호
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• pp.44-51
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• 2001

One of the recent issues in design of the spot-welded structure such as the automobile body is to develop an economical prediction method of the fatigue design criterion without additional fatigue test. In this paper, as one of basic investigation for developing such methods, fracture mechanical approach was investigated. First, the Model I, Mode II and Mode III, stress intensity factors were analyzed. Second, strain energy density factor (S) synthetically including them was calculated. And finally, in order to decide the systematic fatigue design criterion by using this strain energy density factor, fatigue data of the ΔP-N(sub)f obtained on the various in-plane bending type spot-welded lap joints were systematically re-arranged in the ΔS-N(sub)f relation. And its utility and reliability were verified by the theory of Weibull probability distribution function. The reliability of the proposed fatigue life prediction value at 10(sup)7 cycles by the strain energy density factor was estimated by 85%. Therefore, it is possible to decide the fatigue design criterion of spot-welded lap joint instead of the ΔP-N(sub)f relation.

• 품질경영학회지
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• 제26권3호
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• pp.71-81
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• 1998

In reliability analysis, the time difference between the expected next failure time and the current failure time or the Mean Time Between Failure(MTBF) is of significant interest. Until recently, in reliability growth studies, the reciprocal of the intensity function at current failure time has been used as being equal to MTBE($t_n$)at the n-th failure time $t_n$. That is MTBF($t_n$)=l/$\lambda (t_n)$. However, such a relationship is only true for Homogeneous Poisson Process(HPP). Tsokos(1995) obtained the upper bound and lower bound for the MTBF($t_n$) and proposed an estimator for the MTBF($t_n$) as the mean of the two bounds. In this paper, we provide the estimator for the MTBF($t_n$) which does not depend on the value of the shape parameter. The result of the Monte Carlo simulation shows that the proposed estimator has better efficiency than Tsokos's estimator.