• Proceedings of the KSME Conference
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• 2005.11a
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• pp.170.1-170.1
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• 2005

• Journal of Applied Reliability
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• v.5 no.1
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• pp.133-141
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.415-416
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• 2010

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.6
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• pp.649-653
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• 2014

Although many reliability analysis and reliability-based design optimization (RBDO) methods have been developed to estimate system reliability, many studies assume the uncertainty of the design variable to be constant. In practice, because uncertainty varies with the design variable's value, this assumption results in inaccurate conclusions about the reliability of the optimum design. Therefore, uncertainty should be considered variable in RBDO. In this paper, we propose an RBDO method considering variable uncertainty. Variable uncertainty can modify uncertainty for each design point, resulting in accurate reliability estimation. Finally, a notable optimum design is obtained using the proposed method with variable uncertainty. A mathematical example and an engine cradle design are illustrated to verify the proposed method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.518-524
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• 2013

Recently, probabilistic assessments of nuclear power plant components have generated interest in the nuclear industries, either for the efficient inspection and maintenance of older nuclear plants or for improving the safety and cost-effective design of newly constructed nuclear plants. In the present paper, the partial safety factor (PSF) of wall-thinned nuclear piping is evaluated based on a reliability index method, from which the effect of each statistical variable (assessment parameter) on a certain target probability is evaluated. In order to calculate the PSF of a wall-thinned pipe, a limit state function based on the load and resistance factor design (LRFD) concept is first constructed. As for the reliability assessment method, both the advanced first-order second moment (AFOSM) method and second-order reliability method (SORM) are employed to determine the PSF of each probabilistic variable. The present results can be used for developing maintenance strategies considering the priorities of input variables for structural integrity assessments of wall-thinned piping, and this PSF concept can also be applied to the optimal design of the components of newly constructed plants considering the target reliability levels.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.173-179
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• 2018

Estimating reliability of a non-repairable system using the degradation data, variance assumption such as homogeneity (constant) or heteroscedasticity (time-variant) could affect accuracy of reliability estimation. This paper showed reliability estimation and comparison results under normal conditions using accelerated degradation data obtained from destructive measurements, according to variance assumption of the data at each measurement time. Degradation data from three accelerated conditions with stress factors of temperature and humidity were used to estimate reliability. The $B_{10}$ lifetime was estimated as 1243.8 years by constant variance assumption, and 18.9 years by time-variant variance. And variance assumption provided different analysis results of important stresses to reliability. Thus, accurate assumption of variance at each measurement time is required when estimating reliability using degradation data of a non-repairable system.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.5
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• pp.40-48
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• 2018

This paper presents a simulation-based design reliability estimation method of a low-energy exploding foil initiator (LEEFI) using a meta-model and describes the design reliability estimation results. The flyer velocity of the LEEFI is critical to initiate the explosive. Evaluation of the flyer velocity from mechanistic models in open literature requires a long computation time due to the multi-physical phenomena that generate the velocity. Moreover, the higher levels of confidence required for an initiator with high reliability incur higher computation costs. Thus, a meta-model of the flyer velocity over time was constructed in order to increase the computational efficiency for a reliable estimation. For different distributions and sigma levels of the design variables, the design reliability estimation results using the meta-model are provided. Additionally, the computational efficiency and accuracy of the estimation method are analyzed.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.10-19
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• 1996

In order to build a model to predict accidents in a complicated man-machine sytem, human errors and mechanical reliability can be viewed as the most important factors. Such factors are explicitly included in a generic model. Another point to keep in mind is that the model should be constructed so that the data in a type of accident can be utilized to predict other types of accidents. Based on such a generic prediction model, we analyze the effects of system reliability. When we improve the system reliability, in other words, when there are changes in model parameters, the predicted time to next accidents should be modified influencing the effects of system reliability improvements. We apply Bayesian approach and finds the formula to explain how a change on the machine reliability or human error probability influences the time to next accident.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.713-722
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• 2007

Design variables for suspension systems cannot always be realized in the actual suspension systems due to tolerances in manufacturing and assembly processes. In order to deal with these tolerances, design variables associated with kinematic configuration and compliance characteristics of suspensions are treated as random variables. The reliability of a design target with respect to a design variable is defined as the probability that the design target is in the acceptable design range for all possible values of the design variable. To compute reliability, the limit state, which is the boundary between the acceptable and unacceptable design, is expressed mathematically by a limit state function with value greater than 0 for acceptable design, and less than 0 for unacceptable design. Through reliability analysis, the acceptable range of design variables that satisfy a reliability target is specified. Furthermore, through sensitivity analysis, a general procedure for optimization of the design target with respect to the design variables has been established.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.49-60
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• 2011

The geometric process (GP) has been widely used for modeling failure and repair time sequences of repairable systems. The GP is mathematically tractable but restrictive in reliability applications since it actually assumes that the mean function of inter-failure times sequence asymptotically decreases to zero; and the mean function of successive repair times sequence asymptotically increases to infinity. This is generally unrealistic from an engineering perspective. This paper presents three extended GP models for modeling reliability deterioration and improvement (or growth) process. The extensions maintain the advantage of mathematical tractability of GP model. Their usefulness and appropriateness are illustrated with three real-world examples.