• International Journal of Reliability and Applications
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• v.4 no.1
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• pp.27-40
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• 2003

Maintenance models involving minimal imperfect repair frequently appear in the literature of reliability and operations research. Most of the literatures concerning the stochastic behavior of repairable systems assume that it takes negligible time to repair a failed system and so the length of repair time does not affect the maintenance strategy. It is more realistic to consider the length of repair times in developing maintenance model, however. In this paper, we consider an imperfect repair model with random repair time and investigate some stochastic properties of the number of perfect repairs and the number of minimal repairs. Also we derive the expressions for evaluating the expected numbers of perfect and minimal repairs in general and apply these formulas for certain parametric life distributions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.87-92
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• 1995

This paper proposes age replacement policy in stepdown warranty policy. The replacement policy is considered in case of minimally repairable items. And renewal theory is used in analyzing warranty costs. The expected cost per unit time is presented in stepdown warranty policy, free replacement, prorata and hybrid policy. In this article it is assumed that item is replaced at the age of T but the any failure is minimally repaired before the age T. At this point the expected cost per unit time is shown in customer's view point. And numerical example is explored in weibull time-to-failure distribution.

• Earthquakes and Structures
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• v.7 no.6
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• pp.1119-1139
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• 2014

A damage-based seismic design procedure for steel frame structures is formulated as an optimization problem, in which minimization of the initial construction cost is treated as the objective of the problem. The performance constraint of the design procedure is to achieve "repairable" damage state for earthquake demands that are less severe than the design ground motions. The Park-Ang damage index is selected as the seismic damage measure for the quantification of structural damage. The charged system search (CSS) algorithm is employed as the optimization algorithm to search the optimum solutions. To improve the time efficiency of the solution algorithm, two simplifying strategies are adopted: first, SDOF idealization of multi-story building structures capable of estimating the actual seismic response in a very short time; second, fitness approximation decreasing the number of fitness function evaluations. The results from a numerical application of the proposed framework for designing a twelve-story 3D steel frame structure demonstrate its efficiency in solving the present optimization problem.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.119-130
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• 2013

A system with $n$ components which functions when at least ${\kappa}$ of the components function is called ${\kappa}$-out-of-$n$ system. Most studies on ${\kappa}$-out-of-$n$ system derive the system reliability based on the assumption that the system has just two states: functioning or failed. However, the system efficiency may depend on the number of functioning components. This paper considers a Multi-state ${\kappa}$-out-of-$n$ system and derives the total system efficiency. In addition, assuming that the system is repairable, the optimal repair policy to maximize the system efficiency is studied. The system efficiency considered in this paper can be regarded as a generalized measure of the mean time to the failure of the system.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.909-923
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• 2008

This paper considers the replacement model and the preventive maintenance model following the expiration of combination warranty for a repairable system. If the system fails after the combination warranty is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal replacement policy and the optimal preventive maintenance policy is the overall value function based on the expected cost rate per unit time and the expected downtime per unit time. The numerical examples are presented for illustrative purpose when the failure time follows a Weibull distribution.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.111-130
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• 1998

We propose an imperfect repair model which depends on external effects quantified by covariates. The model is based on the Brown-Proschan imperfect repair model wherefrom the probability of perfect repair is represented by a function of covariates. We are motivated by deficiency of the BP model whose stationarity prevents us from predicting dynamically the time to next failure according to external condition. Five types of function for the probability of perfect repair are proposed. This article also presents a procedure for estimating the parameter of the function for the probability of perfect repair, as well as the inherent lifetime distribution of the system, based on consecutive inter-failure times and the covariates. The estimation procedure is based on the expectation-maximization principle which is suitable to incomplete data problems. focusing on the maximization step, we derive some theorems which guarantee the existence of the solution. A Monte Carlo study is also performed to illustrate the prediction power of the model as well as to show reasonable properties of the estimates. The model reduces significantly the mean square error of the in-sample prediction. so it can be utilized in real fields for evaluating and maintaining repairable systems.

• Journal of the Korean Society for Railway
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• v.14 no.3
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• pp.216-221
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• 2011

An important problem in reliability analysis for repairable systems is to model the maintenance effect. The most of researches have assumed two extreme cases; one is perfect maintenance and the other is minimal maintenance. However, many of maintenances performed by domestic subway operators are imperfect maintenances which have the effect between both of two extreme cases. This article deals with the problem determining the imperfect preventive maintenance intervals based on failure data in units of the PDS(passenger door system) in Metro EMU. This paper deals with a case study on determining imperfect maintenance interval by using the level of maintenance effect through reliability analysis of PDS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.24-34
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• 2014

In order to manage essential parts that are required for the repairable parts services performed at the military maintenance depots, the United States Air Force developed the Repairability Forecasting Model (RFM). In the RFM, if the requirements of the parts are assumed to follow the normal probability distribution after applying means from the past data to the replacement rate and lead times, the chance of the AWP (Awaiting Parts) occurring is 50%. In this study, to counter the uncertainties of requirements and lead times from the RFM, the safety level concept is considered. To obtain the safety level for requirements, the binomial probability distribution is applied, while the safety level for lead time is obtained by applying the normal probability distribution. After adding this concept, the improved RFM is renamed as the ARFM (Advanced RFM), and by conducting the numerical stimulation, the effectiveness of the ARFM, minimizing the occurrence of the AWP, is shown by increasing the efficiency of the maintenance process and the operating rate of the weapon system.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.90-100
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• 2015

Reliability growth rate (or reliability growth curve slope) have the two cases of trend as a constant or changing one during the reliability growth testing. The changing case is very common situation. The reasons of reliability growth rate changing are that the failures to follow the NHPP (None-Homogeneous Poisson Process), and the solutions implemented during test to break out other problems or not to take out all of the root cause permanently. If the changing were big, the "Goodness of Fit (GOF)" of reliability growth curve to test data would be very low and then reduce the accuracy of assessing result with test data. In this research, we are using Duane model and AMSAA model for assessing test data and projecting the reliability level of complex and repairable system as like construction equipment and vehicle. In case of no changing in reliability growth rate, it is reasonable for reliability engineer to implement the original Duane model (1964) and Crow-AMSAA model (1975) for the assessment and projection activity. However, in case of reliability growth rate changing, it is necessary to find the method to increase the "GOF" of reliability growth curves to test data. To increase GOF of reliability growth curves, it is necessary to find the proper parameter calculation method of interesting reliability growth models that are applicable to the situation of reliability growth rate changing. Since the Duane and AMSAA models have a characteristic to get more strong influence from the initial test (or failure) data than the latest one, the both models have a limitation to contain the latest test data information that is more important and better to assess test data in view of accuracy, especially when the reliability growth rate changing. The main objective of this research is to find the parameter calculation method to reflect the latest test data in the case of reliability growth rate changing. According to my experience in vehicle and construction equipment developments over 18 years, over the 90% in the total development cases are with such changing during the developing test. The objective of this research was to develop the newly assessing method and the process for GOF level increasing in case of reliability growth rate changing that would contribute to achieve more accurate assessing and projecting result. We also developed the new evaluation method for GOF that are applicable to the both models as Duane and AMSAA, so it is possible to compare it between models and check the effectiveness of new parameter calculation methods in any interesting situation. These research results can reduce the decision error for development process and business control with the accurately assessing and projecting result.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.