• Transactions of The Korea Fluid Power Systems Society
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• v.2 no.2
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• pp.1-7
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• 2005

For the reliability evaluation of the track drive unit(TDU), firstly, we analyzed the major failure modes through FMEA(failure mode & effects analysis), FTA(failure tree analysis), and 2-stage QFD(quality function deployment), and then quantitatively determined the priority order of test items. The Minitab analysis was also performed for prediction of life distribution and parameters of TDU by use of field failure data collected from 430 excavators for two years. In addition, we converted the fluctuation load in field conditions into the equivalent load, and for evaluation of the accelerated lift by the cumulative fatigues, the equivalent load is again divided into the fluctuation load by reference of test time. And then, by use of the test method in this paper, the acceleration factor(AF) of needle bearing inside planetary gear which is the most weakly designed part of TDU is achieved as 5.3. This paper presents the quantitative selection method of test items for reliability evaluation, the determination method of the accelerated life test time, and the method of non-failure test time based on a few of samples. And, we proved the propriety of the proposed methods by experiments using a TDU for a 30 ton excavator.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This 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 for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.295-300
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• 2007

Probabilistic Risk Assessment considering statistically random variables is performed for the preliminary design of a Arch Bridge. Component reliabilities of girders have been evaluated using the response surfaces of the design variables at the selected critical sections based on the maximum shear and negative moment locations. Response Surface Method (RSM) is successfully applied for reliability analyses for this relatively small probability of failure of the complex structure, which is hard to be obtained by Monte-Carlo Simulations or by First Order Second Moment Method that can not easily calculate the derivative terms of implicit limit state functions. For the analysis of system reliability, parallel resistance system composed of girders is changed into parallel series connection system. The upper and lower probabilities of failure for the structural system have been evaluated and compared with the suggested prediction method for the combination of failure modes. The suggested prediction method for the combination of failure modes reveals the unexpected combinations of element failures in significant]y reduced time and efforts compared with the previous permutation method or system reliability analysis method.

• Tunnel and Underground Space
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• v.18 no.6
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• pp.447-456
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• 2008

Open-pit mine slope design must be carried out from the economical efficiency and stability point of view. The overall slope angle is the primary design variable because of limited support or reinforce options available. In this study, the slope angle and critical slope height of large coal mine located in Pasir, Kalimantan, Indonesia were determined from safety point of view. Failure time prediction based on the monitored displacement using inverse velocity was also conducted to make up fir the uncertainty of the slope design. From the study, critical slope height was calculated as $353{\sim}438m$ under safety factor guideline (SF>1.5) and $30^{\circ}$ overall slope angle but loom is recommended as a critical slope height considering the results of sensitivity analysis of strength parameters. The results of inverse velocity analysis also showed good agreement with field slope cases. Therefore, failure of unstable slope can be roughly detected before real slope failure.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.109-120
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• 2013

As information-oriented industry has been developed and electronic devices has come to be smaller, lighter, multifunctional, and high speed, the components used to the devices need to be much high density and should have find pattern due to high integration. Also, diverse reliability problems happen as user environment is getting harsher. For this reasons, establishing and securing products and components reliability comes to key factor in company's competitiveness. It makes accelerated test important to check product reliability in fast way. Out of fine pattern failure modes, failure of Electrochemical Migration(ECM) is kind of degradation of insulation resistance by electro-chemical reaction, which it comes to be accelerated by biased voltage in high temperature and high humidity environment. In this thesis, the accelerated life test for failure caused by ECM on fine pattern substrate, $20/20{\mu}m$ pattern width/space applied by Semi Additive Process, was performed, and through this test, the investigation of failure mechanism and the life-time prediction evaluation under actual user environment was implemented. The result of accelerated test has been compared and estimated with life distribution and life stress relatively by using Minitab software and its acceleration rate was also tested. Through estimated weibull distribution, B10 life has been estimated under 95% confidence level of failure data happened in each test conditions. And the life in actual usage environment has been predicted by using generalized Eyring model considering temperature and humidity by developing Arrhenius reaction rate theory, and acceleration factors by test conditions have been calculated.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.115-120
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• 2012

In order to establish preventive maintenance standards through analysis & reliability prediction of about 60,000pcs of 20kindsof relays and contractors used for Seoul subway trains, several life prediction methodologies were applied. Firstly, Occurrence, Severity, Detection were defined and predicted by applying operation characteristic of EMU to the number of actions of relays & contactors which the manufacturers generally offer as the life cycle data. Secondly, failure distribution and average life of parts were analyzed through interpretation of field data based on a lot of experience which had built up in the field for a long time. Finally, using the 217PLUS standard as a reliability prediction program, comparative analysis of use reliability and inherent reliability was done through reliability prediction at the part level and system level.

• Journal of Biosystems Engineering
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• v.11 no.1
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• pp.76-85
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• 1986

This study was intended to examine the failure characteristics and breakdowns of the head-fed type combines generally used on farms. The failure distribution was assumed to follow Weibull distribution function and the Weibull parameters of the major parts, units and combine as whole were estimated by using the data collected in a survey. A computer program for the estimation of the Weibull parameter was developed. Monte Carlo method was used in predicting the time between failures. The results of study may be summarized as follows: 1. The number of failures per combine was 4.83 times per year and 0.3 times per hectare of combines of different ages. 2. According to the Kolmogorov-Smirnov test method, it was proved that the Weibull distribution function is well fitted to the characteristics of the failure and breakdowns of combines. 3. Weibull parameters of failure distribution of the combine as a whole were estimated to give the shape parameter ${\beta}$=1.3089 and the scale parameter ${\alpha}$=105.2409. The combining area with 80% reliability was 1.1 ha, and the probability of operating the combine without any failure for a year, was $2.76{\times}10^{-4}$. 4. The mean time between failures (MTBF) of the combines was predicted to be 3.2 ha of operation, which corresponds to 32 hours of operation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.