• 산업경영시스템학회지
• /
• 제43권2호
• /
• pp.33-38
• /
• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• 한국수자원학회논문집
• /
• 제40권1호
• /
• pp.39-50
• /
• 2007

본 논문에서는 대수-선형 파손율 모형(log-linear ROCOF)과 와이블 파솔율 모형(Weibull ROCOF)을 이용하여 상수도 주철 배수관로의 파손율을 모형화하고, '수정된 시간 척도'를 이용하여 최적교체시기를 산정할 수 있는 방법이 개발되었다. 두 ROCOF의 모형화를 위하여 개별 관로의 파손시간을 기록한 '파손 시간자료(failure-time data)'와 일정 시간간격 사이에서 발생하는 파손횟수를 기록한 '파손 횟수자료(failure-number data)'를 이용하였고, 최대로그우도 추정값을 이용하여 두 ROCOF의 각 파손자료 유형에 대한 모형화 수행 능력을 검증하였다. 또한 두 ROCOF를 이용한 관로의 최적교체시기 방정식은 ROCOF의 매개변수 추정에 있어서 수렴성을 보장하기 위하여 '수정된 시간 척도'를 적용하여 유도하였다. 연구대상 주철 배수 관로들의 '파손 시간자료'와 '파손 횟수자료'에 두 파손율 모형을 적용시켜 본 결과 파손 시간자료를 이용할 경우 대수-선형 ROCOF가 와이블 ROCOF 보다 적합한 모형인 것으로 나타났다. 또한 두 모형 모두 '파손 시간자료'를 이용하는 것이 '파손 횟수자료'를 이용하는 것보다 모형화 수행 능력이 높아지는 것으로 나타나서, 분석에 사용된 관로의 파손율 모형화와 최적교체시기 산정을 위해서는 일정 시간간격 동안의 관로 파손횟수를 기록하는 것보다 관로의 파손시간을 기록하는 것이 더욱 우수한 모형화 결과를 낳는 것으로 나타났다.

• 융합보안논문지
• /
• 제8권2호
• /
• pp.35-40
• /
• 2008

소트프웨어 고장 시간은 테스팅 시간과 관계없이 일정하거나, 단조 증가 혹은 단조 감소 추세를 가지고 있다. 이러한 소프트웨어 신뢰모형들을 분석하기 위한 자료척도로 자료에 대한 추세 검정이 개발되어 있다. 추세 분석에는 산술평균 검정과 라플라스 추세 검정 등이 있다. 추세분석들은 전체적인 자료의 개요의 정보만 제공한다. 본 논문에서는 고장시간을 측정하다가 시간절단이 될 경우에 미래의 고장 시간 예측에 관하여 연구되었다. 고장 시간 예측에 사용된 고장시간자료는 소프트웨어 고장 시간 분포에 널리 사용되는 와이블 분포에서 형상모수가 1이고 척도모수가 0.5를 가진 난수를 발생된 모의 자료를 이용 하였다. 이 자료를 이용하여 시계열 분석에 이용되는 ARIMA 모형 중에서 AR(1) 모형과 모의실험을 통한 예측 방법을 제안하였다. 이 방법에서 ARIMA 모형을 이용한 예측방법이 효율적임을 입증 하였다.

• 한국군사과학기술학회지
• /
• 제14권5호
• /
• pp.859-870
• /
• 2011

The purpose of this paper is to develop reliability analysis procedures for repairable systems with interval failure time data and apply the procedures for assessing the storage reliability of a subsystem of a certain type of guided missile. In the procedures, the interval failure time data are converted to pseudo failure times using the uniform random generation method, mid-point method or equispaced intervals method. Then, such analytic trend tests as Laplace, Lewis-Robinson, Pair-wise Comparison Nonparametric tests are used to determine whether the failure process follows a renewal or non-renewal process. Monte Carlo simulation experiments are conducted to compare the three conversion methods in terms of the statistical performance for each trend test when the underlying process is homogeneous Poisson, renewal, or non-homogeneous Poisson. The simulation results show that the uniform random generation method is best among the three. These results are applied to actual field data collected for a subsystem of a certain type of guided missile to identify its failure process and to estimate its mean time to failure and annual mean repair cost.

• Journal of the Korean Data and Information Science Society
• /
• 제20권5호
• /
• pp.933-939
• /
• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Communications for Statistical Applications and Methods
• /
• 제23권6호
• /
• pp.467-478
• /
• 2016

The accelerated failure time model or accelerated life model relates the logarithm of the failure time linearly to the covariates. The parameters in the model provides a direct interpretation. In this paper, we review some newly developed practically useful estimation and inference methods for the model in the analysis of right censored data.

• Journal of the Korean Data and Information Science Society
• /
• 제14권4호
• /
• pp.997-1005
• /
• 2003

In this paper, the problem of determining optimal burn-in time is considered under a general failure model. There are two types of failure in the general failure model. One is Type I failure (minor failure) which can be removed by a minimal repair and the other is Type II failure (catastrophic failure) which can be removed only by a complete repair. In this model, when the unit fails at its age t, Type I failure occurs with probability 1 - p(t) and Type II failure occurs with probability p(t), $0{\leq}p(t)\leq1$. Under the model, the properties of optimal burn-in time maximizing mean time to the catastrophic failure during field operation are obtained. The obtained results are also applied to some illustrative examples.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
• /
• pp.9-11
• /
• 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.

• 한국안전학회지
• /
• 제35권1호
• /
• pp.79-86
• /
• 2020

Failure data that has systematically managed for a long time has high reliability to an estimated volume. But since much cost and effort are needed to secure reliability data, data from overseas country is used in quantitative risk analysis in many workplaces. Reliability of the data that can be collected in workplaces can be dropped because of insufficient sample or lack of observation time. Therefore, estimated data is difficult to use as it is and environment and characteristic of the workplace cannot be reflected by using data from overseas country. So this study used Bayesian method that can be used reflecting both reliability data from overseas country and workplace failure data that has less samples. As a setting toward difficult situation that securing sufficient failure data cannot be achieved, we composed workplace failure data equivalent to mass observation time 20%(t=17000), 40%(t=24000), 60%(t=31000), 80%(t=38000) and IEEE data by using Bayesian method.

• ETRI Journal
• /
• 제36권6호
• /
• pp.1023-1031
• /
• 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.