• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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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.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.537-544
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• 2003

The objectives of this study were to investigate the failure characteristics of a total of 90 parts of tractor driveline, and to predict their average annual demands required to perform the after-sales service. The failure characteristics such as failure mode, mean time between failures, characteristic life and reliability were analyzed using the data collected through the experienced mechanics at the part centers of the tractor manufacturers. The analysis was based on the assumption that the failure distribution follows the Weibull distribution. The average annual demands were also predicted for the replacement parts using the mean time between failures and the renewal theory based on the Weibull distribution. The results of the study revealed that the driveline parts failure was mostly from wearout and their average characteristic life is about 1.760 hours. The estimated mean time between failures was in a range of 670∼3,740 hours and reliability in a range of 40∼60％. The annual replacement demands were in a range of 4∼45 for a service of 100 tractors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.145-152
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• 2019

All machines deteriorate in performance over time. The phenomenon that causes such performance degradation is called deterioration. Due to the deterioration, the process mean of the machine shifts, process variance increases due to the expansion of separate interval, and the failure rate of the machine increases. The maintenance model is a matter of determining the timing of preventive maintenance that minimizes the total cost per wear between the relation to the increasing production cost and the decreasing maintenance cost. The essential requirement of this model is that the preventive maintenance cost is less than the failure maintenance cost. In the process mean shift model, determining the resetting timing due to increasing production costs is the same as the maintenance model. In determining the timing of machine adjustments, there are two differences between the models. First, the process mean shift model excludes failure from the model. This model is limited to the period during the operation of the machine. Second, in the maintenance model, the production cost is set as a general function of the operating time. But in the process mean shift model, the production cost is set as a probability functions associated with the product. In the production system, the maintenance cost of the equipment and the production cost due to the non-confirming items and the quality loss cost are always occurring simultaneously. So it is reasonable that the failure and process mean shift should be dealt with at the same time in determining the maintenance time. This study proposes a model that integrates both of them. In order to reflect the actual production system more accurately, this integrated model includes the items of process variance function and the loss function according to wear level.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.535-541
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• 2007

The Non-homogeneous Poisson process is probably the most popular model since it can model systems that are deteriorating or improving. The renewal process is a model that is often used to describe the random occurrence of events in time. But both these models are based on too restrictive assumptions on the effect of the repair action. The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose maximum likelihood estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model.

• International Journal of Reliability and Applications
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• v.5 no.2
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• pp.81-103
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• 2004

Improvements of a bridge network system are studied in this paper. Then equivalence between different improved designs of the bridge network system is discussed. Three different methods are used to get different better designs of the network in the sense of having higher reliability and mean time to failure. Then two different types of reliability equivalence factors of the system are derived. It is assumed here that the failure rates of the system's components are identical and constant. The reliability functions and mean time to failure of the original and improved designs of the network are derived. Comparison between the mean time to failures of the original system and improved designs of the system are presented. Numerical studies and conclusion are presented in order to explain how one can apply the the theoretical results obtained.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.155-162
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• 1999

reliability from components reliability. In this case, it assumes that components failure is mutually independent, but it may not true in real systems. In this study, the mean cost per unit time is computed as the ratio of mean life to the mean cost. The mean life is obtained by the reliability function under power rule model. The mean cost is obtained by the mathematical model based on the inspection interval. A heuristic method is proposed to determine the optimal number of redundant units and the optimal inspection interval to minimize the mean cost per unit time. The assumptions of this study are as following : First, in the load-sharing k-out-of-n:G system, total loads are applied to the system and shared by the operating components. Secondly, the number of failed components affects the failure rate of surviving components as a function of the total load applied. Finally, the relation between the load and the failure rate of surviving components is set by the power rule model. For the practical application of the above methods, numerical examples are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.428-434
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• 2020

The one-shot weapon system is destroyed after only one mission. So, the system requires high reliability. Guided missiles are one-shot weapon systems that have to be analyzed by storage reliability since they spend most of their life in storage. The analysis results depend on the model and the ratio of correct censored data. This study was conducted to propose a method to more accurately predict the future failure rate of Air force guided missiles. In the proposed method, the failure rate is predicted by both MTTF (Mean Time To Failure) and MTBF (Mean Time Between Failure) models and the model with a smaller error from the real failure rate is selected. Next, with the selected model, the ratio of correct censored data is selected to minimize the error between the predicted failure rate and the real failure rate. Based on real field data, the comparative result is determined and the result shows that the proposed sampling rate can predict the future failure rate more accurately.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.1014-1017
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• 2000

Reliability engineering is regarded as the major and important roll for all industry. And advanced manufacturing systems with high speed and intelligent have been developed for the betterment of machining ability. In this study, we have systemized evaluation of reliability for machinery system. We proposed the reliability assessment and design review method using analyzing critical units of high speed and intelligent machine system. In addition, we have not only designed and developed test bed system for acquiring reliability data, but also have constructing WEB system for suppling reliability which is provided in design phase. From this study, we will expect to guide and introduce the reliability engineering in developing and processing phase of high quality product.