• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.41 no.5
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• pp.240-245
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• 2015

Objectives: This study was performed to evaluate patterns of failure time after insertion, failure rate according to loading time after insertion, and the patterns of failure after loading. Materials and Methods: A total of 331 mini-implants were classified into the non-failure group (NFG) and failure group (FG), which was divided into failed group before loading (FGB) and failed group after loading (FGA). Orthodontic force was applied to both the NFG and FGA. Failed mini-implants after insertion, ratio of FGA to NFG according to loading time after insertion, and failed mini-implants according to failed time after loading were analyzed. Results: Percentages of failed mini-implants after insertion were 15.79%, 36.84%, 12.28%, and 10.53% at 4, 8, 12, and 16 weeks, respectively. Mini-implant failure demonstrated a peak from 4 to 5 weeks after insertion. The failure rates according to loading time after insertion were 13.56%, 8.97%, 11.32%, and 5.00% at 4, 8, 12, and 16 weeks, respectively. Percentages of failed mini-implants after loading were 13.79%, 24.14%, 20.69%, and 6.9% at 4, 8, 12, and 16 weeks, respectively. Conclusion: Mini-implant stability is typically acquired 12 to 16 weeks after insertion, and immediate loading can cause failure of the mini-implant. Failure after loading was observed during the first 12 weeks.

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

• ETRI Journal
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• v.36 no.6
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• pp.1023-1031
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• 2014

Failure analysis is necessary to clarify the root cause of a failure, predict the next time a failure may occur, and improve the performance and reliability of a system. However, it is not an easy task to analyze and interpret failure data, especially for complex systems. Usually, these data are represented using many attributes, and sometimes they are inconsistent and ambiguous. In this paper, we present a scalable approach for the analysis and interpretation of failure data of high-performance computing systems. The approach employs rough sets theory (RST) for this task. The application of RST to a large publicly available set of failure data highlights the main attributes responsible for the root cause of a failure. In addition, it is used to analyze other failure characteristics, such as time between failures, repair times, workload running on a failed node, and failure category. Experimental results show the scalability of the presented approach and its ability to reveal dependencies among different failure characteristics.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.9-11
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• 2006

This paper suggests a revision method for historical fault data using Proportional Aging Reduction(PAR) to consider maintenance effect in time-varying failure rate. In order to product time-varying failure rate, the historical fault data are necessary. However, the maintenance record could be left out in historical data by spot operator's mistake. In this case, the failure rate is produced less than the average failure rate for increasing equipments' life-time by maintenance effect. Hence, it is necessary for new time-varying failure rate to extract maintenance effect from the existing fault data. In this paper, the revision method to reduce equipments' life-time, adversely using PAR among three techniques to consider maintenance effect.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.121-130
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• 2021

Cascading failure is the main cause of large blackouts in electrical power systems; this paper analyzes a cascading failure in Hanbit nuclear power plant unit two (2) caused by a circuit breaker (CB) operation failure. This malfunction has been expanded to the loss of offsite power (LOOP). In this study, current practices are reviewed and then the methodologies of how to prevent cascading failures in protection power systems are introduced. An overview on the implementation of IEC61850 GOOSE messaging-based zone selective interlocking (ZSI) scheme as key solution is proposed. In consideration of ZSI blocking time, all influencing factors such as circuit breaker opening time, relay I/O response time and messages travelling time in the communication network should be taken into account. The purpose of this paper is to elaborate on the effect of cascading failure in NPP electrical power protection system and propose preventive actions for this failures. Finally, the expected advantages and challenges are elaborated.

• 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 Applied Reliability
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• v.3 no.2
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• pp.93-101
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• 2003

Life time data analysis requires some time-to-failure data to an extent. Some life tests result in few or no failure. In such cases, it is difficult to access reliability with traditional life tests that record only time to failure. Furthermore, with short product development time, reliability tests must be conducted with severe time constraints. For some devices, it is possible to obtain degradation measurements over time, and these measurements may contain useful information about product reliability. This article describes degradation reliability analysis methods to do inferences and predictions about a failure-time distribution by using software. In addition, the possibility of extension to CBM (Condition Based Maintenance) is suggested as an example of applied degradation data analysis.

• 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 the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.14-20
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• 2009

In this paper, the time-varying failure rate to consider climate effect was extracted. Even if the same kind of equipments is estimated for extracting the time-varying failure rate, the failure rates could be different depending on external effect such as climate. With the consequence, the failure rate extracted to consider the climate effect is necessary for using the failure rate on the optimal investment plan or asset management, To consider the characteristic of climate effects(Classified into 5 categories, heavy rain, thunderbolt, strong wind, tidal waves, no character), the survey of officers charging the operation of equipment in KEPCO branch office was done. With this consequence, this paper suggest the failure rate extraction method to consider the climate effect analyzed by the survey.

• Journal of the Korea Institute of Building Construction
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• v.4 no.2
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• pp.137-142
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• 2004

There was the time of IMF(1998) that management condition of construction business had been the worst. After that time, structural regulation was completed and financial structure was returned to normalcy(2001). At that time, the aim of this paper is that fifteen construction business are researched for process of management condition and capital structure after they is selected as samples for three years, also failure of two-groups is predicted as statistics analysis and multiple discriminant analysis for them. In this paper, It is researched financial statement of business by the forecast experiment of failure and analyzed statistically possibility of failure and success for financial ratio. For them, the fifteen companies of failure and the fifteen companies what were not the failure, for listed company, and the fourteen variables are selected and they are analyzed statistically according to Logit Analysis.