• Convergence Security Journal
• /
• v.8 no.2
• /
• pp.35-40
• /
• 2008

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. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

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

• Convergence Security Journal
• /
• v.12 no.3
• /
• pp.115-121
• /
• 2012

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 offers information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, we predict the future failure time by using the curve regression analysis where the s-curve, growth, and Logistic model is used. The proposed prediction method analysis used failure time for the prediction of this model. Model selection using the coefficient of determination and the mean square error were presented for effective comparison.

• Journal of Korean Society for Quality Management
• /
• v.17 no.2
• /
• pp.17-26
• /
• 1989

This paper is concerned with forecasting the existing number of errors in the computer software and optimizing the stopping time of the software test based upon the forecasted number of errors. The most commonly used models have assessed software reliability under the assumption that the software failure late is proportional to the current fault content of the software but invariant to time since software faults are independents of others and equally likely to cause a failure during testing. In practice, it has been observed that in many situations, the failure rate decrease. Hence, this paper proposes a mathematical model to describe testing situations where the failure rate of software limearly decreases proportional to testing time. The least square method is used to estimate parameters of the mathematical model. A cost model to optimize the software testing time is also proposed. In this cost mode two cost factors are considered. The first cost is to test execution cost directly proportional to test time and the second cost is the failure cost incurred after delivery of the software to user. The failure cost is assumed to be proportional to the number of errors remained in the software at the test stopping time. The optimal stopping time is determined to minimize the total cost, which is the sum of test execution cast and the failure cost. A numerical example is solved to illustrate the proposed procedure.

• Journal of Korean Institute of Industrial Engineers
• /
• v.27 no.3
• /
• pp.233-238
• /
• 2001

This paper deals with demand forecasting of parts in an automobile model which has been extinct. It is important to estimate how much inventory of each part in the extinct model should be stocked because production lines of some parts may be replaced by new ones although there is still demands for the model. Furthermore, in some countries, there is a strong regulation that the automobile manufacturing company should provide customers with auto parts for several years whenever they are requested. The major characteristic of automobile parts demand forecasting is that there exists a close correlation between the number of running cars and the demand of each part. In this sense, the total demand of each part in a year is determined by two factors, the total number of running cars in that year and the failure rate of the part. The total number of running cars in year k can be estimated sequentially by the amount of shipped cars and proportion of discarded cars in years 1, 2,$\cdots$, i. However, it is very difficult to estimate the failure rate of each part because available inter-failure time data is not complete. The failure rate is, therefore, determined so as to minimize the mean squared error between the estimated demand and the observed demand of a part in years 1, 2,$\cdots$, i. In this paper, data obtained from a Korean automobile manufacturing company are used to illustrate our model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.886-890
• /
• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2009.03a
• /
• pp.649-658
• /
• 2009

The Aim of this development is the safety management network of embankment facilities using forecasting analysis algorism. Using this algorithm it is possible to predict a failure of embankment facilities in advance. therefore, it is necessary for making plans of a safety countermove. In this development we have researched the analysis method which could operate effectively the embankment facilities using real-time monitoring data from a remote sensing system and the safety managerial program using the algorithm from the analysis method developed.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.3
• /
• pp.51-55
• /
• 2019

The loss of safety for reservoirs brought about by climate change and facility aging leads to reservoir failures, which results in the loss of lives and property damage in downstream areas. Therefore, it is necessary to provide a Reservoir Failure Forecasting System for downstream residents to detect the early signs of failure (with sensors) in real-time and perform safety management to prevent and minimize possible damage. For the verification of established water level management criteria, 10 water level data up to reservoir capacity was selected. Weight factor and trend line were applied to dramatic increase section of water level in the 1 year period data. The results shows that water level criteria based on three even parts shows less than 7% of standard deviation and it is appropriate to verify management criteria.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.704-715
• /
• 2008

The Aim of this development is the management of a forecasting analysis program based on a real-time remote sensing data. Using this program it is possible to predict a failure of levee facilities in advance. therefor, it is necessary for making plans of a safety countermove. In this development we have researched the analysis method which could operate effectively the levee facilities using real-time monitoring data from a remote sensing system and the safety managerial program using the algorism from the analysis method developed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.234-241
• /
• 2021

By recognizing the importance of demand forecasting, the military is conducting many studies to improve the prediction accuracy for repair parts. Demand forecasting for repair parts is becoming a very important factor in budgeting and equipment availability. On the other hand, the demand for intermittent repair parts that have not constant sizes and intervals with the time series model currently used in the military is difficult to predict. This paper proposes a method to improve the prediction accuracy for intermittent repair parts of the Patriot. The authors collected intermittent repair parts data by classifying the demand types of 701 repair parts from 2013 to 2019. The temperature and operating time identified as external factors that can affect the failure were selected as input variables. The prediction accuracy was measured using both time series models and data mining models. As a result, the prediction accuracy of the data mining models was higher than that of the time series models, and the multilayer perceptron model showed the best performance.