• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.109-120
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• 1993

Most systems are composed of components which have different failure chracteristics. Since the failure characteristics of components is different, it is rational and reasonable to establish a maintenance model to be considered repair and replacement policies which are proper to failure characteristics of these components. This paper proposes the age replacement time for a system composed of components which have different failure characteristics. In this model, it is assumed that a system is composed of a critical failure component, a major failure component, minor failure component. If any failure occurs to critical component before its age replacement time, the system should be replaced. If any failure does not occur until its age replacement time, preventive replacement should be performed at age replacement time T. Major component is minimal repaired if any failure occurs during operation. Minor component should be replaced as soon as failure is found. This paper determines the optimal replacement time of the system which minimize, total maintenance cost and initial stock Quantity of minor component within this optimal replacement time. Numerical example illustrates these results.

• Journal of Environmental Science International
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• v.17 no.11
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• pp.1281-1293
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• 2008

This study aims at the estimation of dam failure time and dam failure scenario analysis of and applied to Yeoncheon Dam which was collapsed August 1st 1999, using HEC-HMS, DAMBRK-FLDWAV simulation model. As the result of the rainfall-runoff simulation, the lancet flood amount of the Yeoncheon Dam site was $10,324\;m^3/sec$ and the total outflow was $1,263.90\;million\;m^3$. For the dam failure time estimation, 13 scenarios were assumed including dam failure duration time and starting time, which reviewed to the runoff results. The simulation time was established with 30 minutes intervals between one o'clock to 4 o'clock in the morning on August 1, 1999 for the setup standard for each case of the dam failure time estimation, considering the arrival time of the flood, when the actually measured water level was sharply raising at Jeongok station area of the Yeoncheon Dam downstream, As results, dam failure arrival time could be estimated at 02:45 a.m., August 1st 1999 and duration time could be also 30 minutes. Those results and procedure could suggest how and when dam failure occurs and analyzes.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.997-1005
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• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 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.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.11a
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• pp.128-133
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• 2000

The objective of this research is to develop real-time failure detection techniques for damage assessment of composite materials using optical fiber sensors. Signals from matrix cracking or fiber fracture in composite laminates are treated by signal processing unit in real-time. This paper describes the implementation of time-frequency analysis such as the Short Time Fourier Transform(STFT) to determine the time of occurrence of failure. In order to verify the performance of the optical fiber sensor for stress wave detection, we performed pencil break test with EFPI sensor and compared it with that of PZT. The EFPI sensor was embedded in composite beam to sense the failure signals and a tensile test was performed. The signals of the fiber optic sensor when damage occurred were characterized using STFT and wavelet transform. Failure detection system detected the moment of failure accurately and showed good sensitivity with the infinitesimal failure signal.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.507-517
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• 1995

The failure rate of an item depends on operational environment. When an item has a chance failure period and a wearout failure period in sequel, the severity of operational environment causes the increase in the slop of wearout failure rate or the increase in the magnitude of chance failure rate. For such a change of operational environment, this paper concerns the change of optimal preventive replacement time. Two preventive replacement policies, age replacement policy and periodic replacement policy with minimal repair, are considered. Investigated properties are: (a) in age replacement policy, optimal preventive replacement time increases as the chance failure rate increases and optimal preventive replacement time decreases as the slope of wearout failure rate increases, and (b) in periodic replacement policy with minimal repair, optimal preventive replacement time increases as the slope of wearout failure rate increases; however, the change of chance failure rate does not alter the optimal preventive replacement time.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.39-50
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• 2007

This paper presents applications of the log-linear ROCOF(rate-of-occurrence-of-failure) and the Weibull ROCOF to model the failure rate of individual cast iron pipes in a water distribution system and provides a method of estimating the economically optimal replacement time of the pipes using the 'modified time-scale'. The performance of the two ROCOFs is examined using the maximized log-likelihood estimates of the ROCOFs for the two types of failure data: 'failure-time data' and 'failure-number data'. The optimal replacement time equations for the two models are developed by applying the 'modified time-scale' to ensure the numerical convergence of the estimated values of the model parameters. The methodology is applied to the case study water distribution cast iron pipes and it is found that the log-linear ROCOF has better modeling capability than the Weibull ROCOF when the 'failure-time data' is used. Furthermore, the 'failure-time data' is determined to be more appropriate for both ROCOFs compared to the 'failure-number data' in terms of the ROCOF modeling performances for the water mains under study, implying that recording each failure time results in better modeling of the failure rate than recording failure numbers in some time intervals.

• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.33-42
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• 1990

This paper proposes the maintenance model of multi-component system when the failure characteristics and types of components are considered. In this model, it is assumed that a system is composed of a critical component, a major component and a minor component. Also, failure types is classified into major failure and minor failure. If major failure occurs to critical component before system age replacement time, the system is renewed. If major failure does not occur until its age replacement time, preventive maintenance is performed at age replacement time T. Minimal repairs are carried out after each minor failure. Major component is minimal-repaired if any failure is discovered during operation. Minor component should be replaced as soon as any failure is found. This paper determines the optimal replacement time of the system which minimizes total maintenance cost. Numerical example illustrates these results.

• 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.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.387-395
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• 2000

Circular consecutive-k-out-of-n:F system when the failure of component is dependent is studied. We assume that the failure of a component in the system increase the failure rate of the survivor which is working just before the failed component. In this case, a mean time to failure (MTTF), a average failure number of the system, and the expected cost per unit time are obtained. Then the minimum number of consecutive failed components to cause system failure to minimize the expected cost per unit time is determined as searching paths to system failure. And various numerical examples are studied.