• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Journal of IKEEE
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• v.23 no.4
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• pp.1218-1223
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• 2019

In HVDC systems, the full-bridge submodule increases the number of components compared to the half-bridge submodule, but the failure-rate can be reduced by securing 100 % redundancy. However, full-bridge submodules require more complex control algorithms to ensure the redundancy and to prevent arm-short with sufficient dead-time. To solve this problem, we analyse the failure-rate of the paralleled half-bridge configuration with the same number of components and 100 % redundancy as the full-bridge submodule. The fault tree analysis (FTA) method is applied to the conventional part failure analysis to reflect the operation risk of the submodule, thereby predicting the life-cycle of the submodule more accurately. To verify the validity, the failure-rate results of the proposed FTA based analysis method are compared with the failure rate obtained by the part failure method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.253-262
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• 2010

In this study, localized heavy rainfall occurred during the collapse of steep slopes adjacent to the construction site and to ensure the safety of residents to build an early warning system was performed. Forecast/Alert range was estimated based on vulnerability landslide map and past disaster history. And established a critical line in consideration of the characteristics of local rainfall and operating a snake line, the study calculated causing and non-causing points. Also, be measured in real-time analysis of rainfall data in conjunction with the system before the steep slope failure occurred forecast/Alert System is presented.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1195-1202
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• 2012

In this paper, we determine the expected total cost from the user's perspective for the replacement model with the extended warranty when minimal repair cost is a function of failure time. To do so, we define the extended warranty and assume the replacement model following the expiration of extended warranty from the user's perspective. Especially, we propose the criterion to buy the extended warranty and the numerical examples are presented to illustrate the purpose when the failure time of the system has a Weibull distribution.

• Journal of Power System Engineering
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• v.14 no.6
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• pp.61-66
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• 2010

Engineering asset management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting time-to-failure and the probability of failure in future time are essential. In general reliability models, lifetime of component and system is estimated using failure time data. This paper deals with the reliability assessment of elevators using life of main components. Especially this work is concerned with the stochastic nature of life of elevator components. First, we investigate the Weibull statistical analysis of lifetime data for the components. The final goal is to establish the mathematical model for reliability assessment. This work provides more perspectives to future research in the fields of reliability and maintainability.

• Journal of Welding and Joining
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• v.12 no.1
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• pp.80-93
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• 1994

The microstructure and fractography of the friction welded joint of Al to Cu have been investigated in order to understand the formation of intermetallic compounds and their effects on the failure in tensile test of the joint. The variation of welding pressure did not affect significantly the tensile strength of joint. However, the tensile strength of joint decreaed as welding time increased. The thickness of reaction layers of welded joints was several micro-meters and mainly composed of intermetallic compounds of $CuAl_2$, $Cu_9Al_4$ and Al+$CuAl_2$. The thickness of $CuAl_2$, $Cu_9Al_4$ was increased with welding time. However, $CuAl_2$ was gradually changed to $Cu_9Al_4$ which caused the decrease of tensile strength . Even though the morphology of fractured surfaces depended upon the welding time, the failure occurred along $CuAl_2$ intermetallic compound itself or between $CuAl_2$ and $Cu_9Al_4$ in most cases.

• Computers and Concrete
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• v.22 no.3
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• pp.291-303
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• 2018

As the concept of "sponge city" is proposed, the pervious concrete for permeable pavement has been widely used in pavement construction. This paper aims at investigating the dynamic behavior and energy evolution of pervious concrete under impact loading. The dynamic compression and split tests are performed on pervious concrete by using split Hopkinson pressure bar equipment. The failure criterion on the basis of incubation time concept is used to analyze the dynamic failure. It is demonstrated that the pervious concrete is of a strain rate sensitive material. Under high strain rate loading, the dynamic strength increases while the time to failure approximately decreases linearly as the strain rate increases. The predicted dynamic compressive and split tensile strengths based on the failure criterion are in accordance with the experimental results. The total damage energy is found to increase with the increasing of strain rate, which means that more energy is needed to produce irreversible damage as loading rate increases. The fractal dimensions are observed increases with the increasing of impact loading rate.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.116-122
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• 2014

This study proposes a system reliability analysis of rack storage facilities subjected to forklift colliding events. The proposed system reliability analysis consists of two steps: the first step is to identify dominant failure modes that most contribute to the failure of the whole rack facilities, and the second step is to evaluate the system failure probability. In the first step, dominant failure modes are identified by using a simulation-based selective searching technique where the contribution of a failure mode to the system failure is roughly estimated based on the distance from the origin in the space of the random variables. In the second step, the multi-scale system reliability method is used to compute the system reliability where the first-order reliability method (FORM) is initially used to evaluate the component failure probability (failure probability of one member), and then the probabilities of the identified failure modes and their statistical dependence are evaluated, which is called as the lower-scale reliability analysis. Since the system failure probability is comprised of the probabilities of the failure modes, a higher-scale reliability analysis is performed again based on the results of the lower-scale analyses, and the system failure probability is finally evaluated. The illustrative example demonstrates the results of the system reliability analysis of the rack storage facilities subjected to forklift impact loadings. The numerical efficiency and accuracy of the approach are compared with the Monte Carlo simulations. The results show that the proposed two-step approach is able to provide accurate reliability assessment as well as significant saving of computational time. The results of the identified failure modes additionally let us know the most-critical members and their failure sequence under the complicated configuration of the member connections.