• 대한조선학회논문집
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• 제60권6호
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• pp.441-449
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• 2023

With the enforcement of environmental regulations by the International Maritime Organization, the market for eco-friendly ships is expanding, and ships using electric propulsion devices are emerging as a promising solution. Many studies have been conducted to predict the failure of ships, but most of them are mainly research on the main diesel engine of ships. As the ship's propulsion method changes, new data is needed to predict the failure of electric propulsion ships. In this paper aims to analyze the failure characteristics of the electric propulsion system in consideration of the difference in the type of failure between the internal diesel engine and the electric propulsion system. The ship's propulsion unit assumed a DC motor and a signal pattern for normal conditions and general failure modes, but the failure record of the electric propulsion device operated on the actual ship was not available, so it generated a failure signal for small electric motor equipment to identify the failure signal. Assuming unbalance, misalignment, and bearing failure, which are the primary failure modes of the ship's electric motor, a failure signal was generated using a "rotator vibration data generator," and the frequency band, size, and phase difference of the measured vibration signal were analyzed to analyze the characteristics of each failure condition. Finally, the characteristics of each failure condition were identified so that the signals according to the failure type could be classified.

• 한국군사과학기술학회지
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• 제21권5호
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• pp.599-606
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• 2018

Failure data of systems in many field can be erroneous, which influences the reliability analysis of the systems. The general form of failure data is right censored data with accurate time information. But due to its nature of data collection in the military field, failure time of one-shot weapon systems can have errors which are related to the maintenance period. So this paper suggests a model that can reduce the error by utilizing interval censored data as an alternative to right censored data in weibull distribution.

• 시스템엔지니어링학술지
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• 제10권1호
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• pp.43-48
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• 2014

The door system for railway vehicles is the critical device directly influences on safety and satisfaction of passengers, Recently, electrical type of passenger door system is widely used for EMU type train instead of pneumatic type of passenger door system. The estimation of MTBF and failure rates for electrical type door system is essential. The manufacturor simply provides intrinsic reliability data for the railway operator. But actual reliability data based on operation and maintenance data is not complying with intrinsic reliability. In this study, operation and failure data associated with electrical door system were analyzed in order to determine actual MTBF and failure data. Intrinsic reliability data and service reliability data were studied to finallize much more practical and reliable actual reliability. Relax 2011 was used to predict intrinsic reliability and 217Plus model was also used to estimate of actual reliability data based on field data. Furthermore, it is necessary to keep studying on reliability prediction methodology and applying it in the field and doing research on improvement of reliability through feedback as well.

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.173-177
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• 2010

It is well known that if the parent distribution has a nonnegative support and has increasing failure rate, then all the order statistics have increasing failure rate (IFR). The result is not necessarily true in the case of bivariate distributions with dependent structures. In this paper we consider a symmetric bivariate exponential distribution and show that, two marginal distributions are IFR and the distributions of the minimum and maximum are constant failure rate and IFR, respectively.

• Journal of the Korean Data and Information Science Society
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• 제22권4호
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• pp.755-764
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• 2011

기업은 종종 과거의 필드 고장 데이터를 이용하여 미래에 필드에서 고장이 얼마나 일어날 것인지 예측한다. 특히 이런 예측은 필드에서 예기치 않던 고장모드 (failure mode)가 발견될 때 더욱 하고 싶어진다. 왜냐하면 기업은 이런 예측을 통해 미래에 품질보증 비용이 얼마나 될 것인지 파악하고, 고장 난 부품을 재빨리 수리하는데 필요한 여유 부품의 수를 파악하고 싶기 때문이다. 본 연구에서는 기업에서 생길 수 있는 요약 데이터를 사용하여 미래 필드에서 고장이 얼마나 발생할 것인지 예측하고, 이런 요약 데이터이외에 또 어떤 데이터가 생길 수 있으며 이때 분석결과가 어떻게 나올 수 있는지 알아본다.

• Earthquakes and Structures
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• 제19권6호
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• pp.471-484
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• 2020

Previous earthquakes show that, structural safety evaluations should include the evaluation of nonstructural components. Failure of nonstructural components can affect the operational capacity of critical facilities, such as hospitals and fire stations, which can cause an increase in number of deaths. Additionally, failure of nonstructural components may result in economic, architectural, and historical losses of community. Accelerations and random vibrations must be under the predefined limitations in structures with high technological equipment, data centers in this case. Failure of server equipment and anchored server racks are investigated in this study. A probabilistic study is completed for a low-rise rigid sample structure. The structure is investigated in two versions, (i) conventional fixed-based structure and (ii) with a base isolation system. Seismic hazard assessment is completed for the selected site. Monte Carlo simulations are generated with selected parameters. Uncertainties in both structural parameters and mechanical properties of isolation system are included in simulations. Anchorage failure and vibration failures are investigated. Different methods to generate fragility curves are used. The site-specific annual hazard curve is used to generate risk curves for two different structures. A risk matrix is proposed for the design of data centers. Results show that base isolation systems reduce the failure probability significantly in higher floors. It was also understood that, base isolation systems are highly sensitive to earthquake characteristics rather than variability in structural and mechanical properties, in terms of accelerations. Another outcome is that code-provided anchorage failure limitations are more vulnerable than the random vibration failure limitations of server equipment.

• 한국군사과학기술학회지
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• 제16권5호
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• 상하수도학회지
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• 제10권1호
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• pp.96-111
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• 1996

The failure of water pipelines is progressed by several compound factors and the collection and analysis of data about pipeline failure are inevitable for effective pipeline rehabilitation. Data analysis of pipeline failure was already performed in USA and Europe. Based on such phenomena, failure characteristics about metropolitan pipelines in Korea were analyzed: The conclusions of this study are as followings. 1. The failure cause of pipelines can be classified into natural cause and artificial cause. Artificial cause is 32% of total causes, so artificial failure as several constructions happens frequently in Korea. Although the failure by old pipe is greatest of any other causes m classtfied cause, failure cause is not classified in detail now. 2. The damaged part of pipelines is affected by cities, distribution system inventory, bedding conditions, and so on. In this study, the failure of pipeline body(67%) is greater than the failure of pipeline joint(33%) in natural failure. 3. In regard to pipe materials, failure rate of DCIP(0.8456), PEP(0.7288), and GSP(0.6643) is greater than that of CIP(0.3985) and CWSP(0.2348). 4. Usually, faIlure rate is increased in proportion to diameter of pipeline. In this study, CIP, DCIP, and CWSP have clear trends. But the trends of PEP is reverse, the case of GSP, HP is obscure due to data shortage. 5. There are no direct relationships between burial age and failure rate of pipelines. 6. Annual breaks and winter(Nov.~Feb.) breaks of pipelines are investigated. As a result, WInter breaks to annual breaks of CIP is 51.3%(Seoul), 51.1%(Taegu),38.7%(Pusan). This phenomena have direct correlation with average winter temp. of cities.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.617-621
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• 2003

The purpose of this paper is to analyze piping failure trend of safety pipings In domestic nuclear power plants. First, database for the piping failure was constructed with 105 data fields. The database includes plant population data, event data, and service history data. 7 kinds of piping failures in domestic NPPs were investigated. Among the 7 cases, detailed root causes were investigated for 3 cases. The first one is pipe wall thinning in main feedwater pipings of Westinghouse 3 loop type plants. The root cause of the wall thinning was flow accelerated corrosion near welding area. The next one is leak event in chemical and volume control system(CVCS) due to vibration. Some cracks occurred in socket welding area. The events showed that the integrity or socket weld is very vulnerable to vibration. The last one is also a leak event in primary sampling line in Korean standard reactor due to thermal fatigue. Although the structural integrity was not maintained by the events, there was no effect on nuclear safety in the above 3 piping failure eases.

• 경영과학
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• 제11권2호
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• pp.1-27
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• 1994

A reliability data processing MPRDP (Multi-Purpose Reliability Data Processor) has been developed in FORTRAN language since Jan. 1992 at KAERI (Korean Atomic Energy Research Institute). The purpose of the research is to construct a reliability database(plant-specific as well as generic) by processing various kinds of reliability data in most objective and systematic fashion. To account for generic estimates in various compendia as well as generic plants' operating experience, we developed a 'three-stage' Bayesian procedure[1] by logically combining the 'two-stage' procedure[2] and the idea for processing generic estimates[3]. The first stage manipulates generic plant data to determine a set of estimates for generic parameters,e.g. the mean and the error factor, which accordingly defines a generic failure rate distribution. Then the second stage combines these estimates with the other ones proposed by various generic compendia (we call these generic book type data). This stage adopts another Bayesian procedure to determine the final generic failure rate distribution which is to be used as a priori distribution in the third stage. Then the third stage updates the generic distribution by plant-specific data resulting in a posterior failure rate distribution. Both running failure and demand failure data can be handled in this code. In accordance with the growing needs for a consistent and well-structured reliability database, we constructed a generic reliability database by the MPRDP code[4]. About 30 generic data sources were reviewed and available data were collected and screened from them. We processed reliability data for about 100 safety related components frequently modeled in PSA. The underlying distribution for the failure rate was assumed to be lognormal or gamma, according to the PSA convention. The dependencies among the generic sources were not considered at this time. This problem will be approached in further study.