• Structural Engineering and Mechanics
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• v.3 no.2
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• pp.135-144
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• 1995

Since structural systems may fail in any one of several failure modes, computation of system reliability is always difficult. A method using numerical quadrature for computing structural system reliability with either one or more than one failure mode is presented in this paper. Statistically correlated safety margin equations are transformed into a group of uncorrelated variables and the joint density function of these uncorrelated variables can be generated by using the Maximum Entropy Method. Structural system reliability is then obtained by integrating the joint density function with the transformed safety domain enclosed within a set of linear equations. The Gaussian numerical integration method is introduced in order to improve computational accuracy. This method can be used to evaluate structural system reliability for Gaussian or non-Gaussian variables with either linear or nonlinear safety boundaries. It is also valid for implicit safety margins such as computer programs. Both the theory and the examples show that this method is simple in concept and easy to implement.

• Journal of Integrative Natural Science
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• v.6 no.2
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• pp.111-117
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• 2013

We propose a mean value function for software failures in NHPP software reliability model. And we deal with the maximum likelihood estimation and the least squares estimation in the proposed mean value function. The explicit mean value function solution for the proposed model is presented by MLE and LSE in two data sets. The values of SSE and MSE is presented in two data sets by MLE and LSE. We compare the predicted number of faults with the actual two data sets using the proposed mean value function.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1662-1669
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• 2006

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.104-118
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• 2003

It has been criticized that conventional human reliability analysis (HRA) methodologies for probabilistic safety assessment (PSA) have been focused on the quantification of human error probability (HEP) without detailed analysis of human cognitive processes such as situation assessment or decision-making which are crticial to successful response to emergency situations. This paper introduces a new human reliability analysis (HRA) methodology, AGAPE-ET (A guidance And Procedure for Human Error Analysis for Emergency Tasks), focused on the qualitative error analysis of emergency tasks from the viewpoint of the performance of human cognitive function. The AGAPE-ET method is based on the simplified cognitive model and a taxonomy of influencing factors. By each cognitive function, error causes or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and influencing mechanism of PIFs on the cognitive function. Then, overall human error analysis process is designed considering the cognitive demand of the required task. The application to an emergency task shows that the proposed method is useful to identify task vulnerabilities associated with the performance of emergency tasks.

• International Journal of Reliability and Applications
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• v.1 no.2
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• pp.133-143
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• 2000

Data from weighted distributions appear, among other situations, when some of the data are missing or are damaged, a case that is important in reliability and life testing. The kernel method for hazard rate estimation is discussed for these data where the basic large sample properties are given. As a by product, the basic properties of the kernel estimate of the distribution function for data from weighted distribution are presented.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.153-160
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• 2008

This paper presents the log likelihood function for integrated models for ALT such as exponential-general Eyring, Weibull-temperature and specific heat, lognormal-temperature and specific heat. Additionally this paper estimates the system reliability and mean time to failure(MTTF) for series, parallel, k of n, and standby system using ALT linkage parameter. Lastly this study designs three variable reliability acceptance sampling(RAS) plans such as type I, II censored test, sequential test by the use of integrated models for ALT.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.79-90
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• 1999

In this paper, we suggested system reliability inequality used by failure rate distribution and developed new theorem-reliability function is increasing function. Also we calculated failure probability of coherent system used by variable transformation. Several examples are illustrated.

• International Journal of Reliability and Applications
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• v.9 no.2
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• pp.153-165
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• 2008

This paper discusses the reliability equivalences of a series-parallel system. The system components are assumed to be independent and identical. The failure rates of the system components are functions of time and follow Weibull distribution. Three different methods are used to improve the given system reliability. The reliability equivalence factor is obtained using the reliability function. The fractiles of the original and improved systems are also obtained. Numerical example is presented to interpret how to utilize the obtained results.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.569-576
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• 2017

The electromagnetic valve of pneumatic doors of EMUs has a high failure rate due to air leakage because it supplies air on and off to operate the doors repeatedly. The electromagnetic valve is a very important safety component for which a very high reliability is required because failure makes it impossible to operate the passenger cars. However, domestic urban railway operators maintain electronic valves of the EMU door under a fixed cycle with a spare period according to the full overhaul cycle of the EMU. An improvement of the current maintenance cycle was suggested based on the reliability function and mission reliability. Using the statistical program MINITAB for the operational data of EMU line 6, we analyzed the characteristics of the fault distribution and derived the shape and scale parameters of the reliability function. If we limit the specific reliability probability to under a certain failure rate and calculate its statistical parameters, we can calculate the allowable inspection period with mission reliability. Through this study, we suggested a maintenance period based on RCM (reliability centered-maintenance) to improve the reliability of electromagnetic valves from 68% to 95%.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.119-126
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• 2010

The software reliability growth model(SRGM) has been developed in order to estimate such reliability measures as remaining fault number, failure rate and reliability for the developing stage software. Almost of them assumed that the faults detected during testing were eventually removed. Namely, they have studied SRGM based on the assumption that the faults detected during testing were perfectly removed. The fault removing efficiency, however, is imperfect and it is widely known as so in general. It is very difficult to remove detected fault perfectly because the fault detecting is not easy and new error may be introduced during debugging and correcting. Therefore, We want to study imperfect software testing effort for the logistic testing effort which is thought to be the most adequate in this paper.