• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.441-444
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• 2007

The mechanical endurance is the critical characteristic of Magnetic contactors (MCs), which are widely used in such industrial equipments as elevators, cranes, and factory control rooms in order to close and open the control circuit. Testing time, however, is so long in most cases that some method of reducing the testing period is required. Therefore, the degradation test by the detected vibration of MCs is developed to reduce the testing time in this work. The degradation test data are analyzed and the prediction model is provided. Also, the possibility of this technology for Reliability Centered Maintenance (RCM) will be shown. This will reduce the period of the product development and raise the reliability of the equipment in power distribution.

• 한국신뢰성학회지:신뢰성응용연구
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• 제11권4호
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• pp.387-398
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• 2011

$217Plus^{TM}$, a newly developed as a surrogate of the MIL-HDBK-217, may be widely applied for reliability predictions of electronic systems. In this study, we performed sensitivity study of the $217Plus^{TM}$ system model to various parameters. Specific attention was put to logistics model and its behavior has been examined in terms of non-component failure causes. We first briefly explained the $217Plus^{TM}$ methodology with system level failure rate evaluation. We then applied experimental designs with several failure causes as factors. We used an orthogonal array with three levels of each parameter. Our results indicate that cannot duplicate, induced, and wear-out causes have dominant effects on the system failures and design, parts, and system management have much less but a little strong effects. The results in this study not only figure out the behavior of the predicted failure rate as functions of failure causes but provide meaningful guidelines for practical applications.

• Smart Structures and Systems
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• 제22권2호
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• pp.193-200
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• 2018

From the viewpoint of engineering applications, the prediction of the failure of bogies plays an important role in preventing the occurrence of fatigue. Fatigue is a complex phenomenon affected by many uncertainties (such as load, environment, geometrical and material properties, and so on). The key to predict fatigue damage accurately is how to quantify these uncertainties. A Bayesian model is used to account for the uncertainty of various sources when predicting fatigue damage of structural components. In spite of improvements in the design of fatigue-sensitive structures, periodic non-destructive inspections are required for components. With the help of modern nondestructive inspection techniques, the fatigue flaws can be detected for bogie structures, and fatigue reliability can be updated by using Bayesian theorem with inspection data. A practical fatigue analysis of welded bogies is utilized to testify the effectiveness of the proposed methods.

• 한국철도학회논문집
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• 제9권6호
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• pp.725-731
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• 2006

Hot Standby sparing system detecting faults by using software, and being tolerant any faults by using Hardware Redundancy is difficult to perform quantitative reliability prediction and to detect real time faults. Therefore, this paper designs Hot Standby sparing system using hardware basis self checking logic in order to overcome this problem. It also performs failure mode analysis of Hot Standby sparing system with designed self checking logic by using FMEA (Failure Mode Effect Analysis), and identifies reliability assessment of the controller designed by quantifying the numbers of failure development by using FTA (Fault Tree Analysis)

• 한국산업정보학회논문지
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• 제7권2호
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• pp.33-39
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• 2002

Accelerated life testing of a car is used to get information quickly on its life distribution. Test cars are no under severe conditions and fail sooner than under usual conditions. A model is fitted to the accelerated failure times and then extrapolated to estimate the life distribution under usual conditions. This paper presents an accelerated test md the reliability growth theory, and applies it to some subsystems of cars during their prototype and pilot testing. The data presented illustrates explicitly the prediction of the reliability growth in the product development cycle. The application of these techniques is a part of the product assurance function that plays an important role in product reliability improvement.

• 한국신뢰성학회지:신뢰성응용연구
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• 제9권4호
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• pp.291-302
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• 2009

Accelerated and field degradation tests are performed for reliability assessment of an anticorrosive paint for steel structures. Test data were analyzed to obtain the degradation model and the life time distributions of the paint. A power law degradation model and lognormal performance distribution were used to predict the lifetime of the anticorrosive paint and the method of finding an acceleration factor is provided.

• 대한전기학회논문지:전력기술부문A
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• 제55권11호
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• pp.476-484
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• 2006

The failure prediction and preventive maintenance for the equipment of nuclear power plant area using reliability-centered maintenance have been grown. On the other hand, the maintenance for power distribution system consists of time-based maintenance mainly. In this paper, the new maintenance algorithms for power distribution system are developed considering reliability indices. First of all, Time-varying failure rates are extracted from data accumulated at KEPCO using exponential distribution function and weibull distribution function. Next, based on the extracted failure rate, reliability for real power distribution system is evaluated for applying the effective maintenance algorithm which is the analytic method deciding the maintenance point of time and searching the feeder affecting the specific customer. Also the algorithm deciding the maintenance priority order are presented based on sensitivity analysis and equipment investment plan are analyzed through the presented algorithm at real power distribution system.

• 한국항공운항학회지
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• 제4권1호
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• pp.7-24
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• 1996

A methodology has been developed for predicting aircraft reliability incorporating both C.C.F.s(Common-Cause Failures), and phased missions. Failure behaviour of an aircraft, or it's systems are predicted. Both independent failures, and C.C.F.s, are modelled by the Markov process, and simulated using Monte Carlo sampling with the robust variance reduction method. Prediction of safety and reliability is made through discrete-event simulation of aircraft operations. A case study is described for investigating the safety and reliability of the propulsion system of two-, three- and four-engined aircraft. This is particularly important for the design of ETOPS(Extended Range of Two-Engined Aircraft Operations) and results are presented for the cases with, and without the effect of C.C.F.s.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.255-262
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• 2008

The railway is required to be highly reliable, which carries a lot of passenger and baggage. Presently, the reliability prediction method is based on independent failure. If the common cause failure affecting many components simultaneously in a system occurs, the system has seriously an aptitude to be broken out. Therefore, for raising the reliability of the railway power system, it is introduced that the analysis is conducted to use the common cause failure. The common cause failure is modeled and is combined with independent failure. Furthermore in order to examine the method, it is applied to the railway power substation. If this method is used to the power system, the reliability of the railway power system will be highly improved.

• Journal of information and communication convergence engineering
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• 제5권2호
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.