• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.203-211
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• 1999

수리 가능한 시스템의 평균고장간격시간에 대한 많은 연구들이 진행되어 왔으며, 그 대부분은 n번째 고장발생시각 $T_n$을 관측한 후 그 다음 고장이 발생할 때까지의 평균시간, 즉 E($T_{n+1}$-$T_n$$\mid$$T_n$ = $t_n$)에 관한 연구들이었다. 본 연구에서는 수리가능한 시스템의 고장이 와이블과정을 따라 일어날 경우, n번째와 n+1번째 고장간의 평균고장간격시간 E($T_{n+1}$-$T_n$)에 대한 불편추정량을 구하고 일치성 및 근사적 정규성을 증명하였다.

• Convergence Security Journal
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• v.11 no.3
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• pp.19-24
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• 2011

Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.205-218
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• 1996

본 연구는 써버의 고장을 허용하는 단수써버 Queueing 시스템의 확률적 모델을 제시한 것으로, 써버는 N 제어 정책에 의하여 작동되며, 도착은 Stationary compound poisson에 의하여 이루어지고, 서비스 시간에 대한 분포는 Erlang에 의하여 발생하며, 수리시간에 대한 분포는 평균이 일정한 분포에 의하여 생성되는 경우를 고려하였다. 또한 고장간격 시간은 일정한 평균을 가진 임의의 분포를 가진 Renewal process에 의한다고 가정하였고, 완료 시간의 개념은 재생과정의 적용방법에 의하여 유도할 수 있으며, 시스템 크기의 확율 생성 함수의 값이 구해진다는 것을 제시하였다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

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 that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.53-64
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• 1998

Recently, it is of great interest among engineers and reliability scientists to consider a statistical model to describe the failure times of various types of repairable systems. The main subject we deal with in this paper is the power law process which is proved to be a useful model to describe the reliability growth of the repairable system. In particular, we derive the bootstrap confidence intervals of the mean time between two successive failures of a repairable system using the time truncated data. We also compare our bootstrap confindence intervals with Crow's (1982) confidence interval.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.162-162
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• 2003

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.216-224
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• 1999

In the paper we propose two recovery methods by adopting a rollback and/or roll-forward technique (S) to recover TMR failures in a TMR (structured ) system that is the simplest spatial redundancy. This technique is apparently effective to recovering TMR failures primarily caused by transient faults. The proposed policies carry out few reconfigurations at the cost of (minimal) time-overhead needed for those time-redundant schemes. The optimal checkpoint-interval vectors are derived for both methods through the likelihoods of all (possible) states of the system as well as the total execution-time. Consequently the effectiveness of our proposed policies is validated through certain numerical examples and simulations.

• Convergence Security Journal
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• v.7 no.2
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• pp.49-55
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• 2007

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. From the subdivision of this analysis, new attemp needs the side of the quality control. In this paper, we discuss process capability analysis using process capability indexs. Because of software failure interval time is pattern of nonnegative value, instead of capability analysis of suppose to normal distribution, capability analysis of process distribution using to Box-Cox transformation is attermpted. The used software failure time data for capability analysis of process is SS3, the result of analysis listed on this chapter 4 and 5. The practical use is presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.781-787
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• 2010

Forecasting possible failure characteristics is very important in maintenance planning because it helps in predicting any future failures and determining the optimum replacement interval. This paper examines the time.to-failure distribution of the transfer gearbox of a J79 engine by using a probability plotting technique which is one of the most convenient techniques for reliability analysis. Various probability distributions are evaluated for determining the suitable probability distribution of the failure data of the transfer gearbox, and the resulting correlation coefficient indicates that failure data have a lognormal distribution. The expected number of unscheduled maintenance actions and the optimum replacement interval for various values of cost ratios are determined.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.90-96
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• 2000

The technology of preventive maintenance and corrective maintenance is widely applied to ships in order to maintain the good voyageable condition. One of the most important fields of marine engineering is to seek the maximum availability and to solve the stochastic maintenance problem such that the cost for corrective maintenance is minimized. Accordingly, for the purpose of making the most suitable maintenance schedule which minimizes the expected cost function, this paper suggests the method to grasp the failure characteristics by the ship's maintenance data that are collected from the past. And, suggests the method to estimate the optimal maintenance interval by using the dynamic programming and the cumulative hazard rate function attained from the maintenance data.