• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2004년도 정기학술대회
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• pp.343-369
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• 2004

$\bullet$ Due to limited technical sources in small and medium size companies, their mechanical components are being undervalued in Korea. $\bullet$ The domestic large enterprises make limited profit due to their dependency on a great deal of imported components.(omitted)

• International Journal of Reliability and Applications
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• 제7권2호
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• pp.111-125
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• 2006

This paper introduces different vectors of the reliability equivalence factors of a series system consists of n independent and nonidentical components. The failure rates of the system components are assumed to be constant. The reliability function and mean time to failure are used as performances to derive the reliability equivalences of the system. The results presented here generalize those available in the literatures. Numerical study is given to explain how one can utilize the theoretical results obtained.

• Structural Engineering and Mechanics
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• 제23권5호
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• pp.455-468
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• 2006

Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is wellknown that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework.

• 한국항공운항학회지
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• 제17권1호
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• pp.24-28
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• 2009

The reliability assessment is performed for Pressure Control Regulator of Aircraft Fuel System using reliability procedure which consists of the reliability analysis and the Failure Modes and Effects Analysis(FMEA). The target reliability as MTBF(Mean Time Between Failure) is set to 5000hr. During the reliability analysis process, the system is categorized by Work Breakdown Structure(WBS) up to level 3, and a reliability structure is defined by schematics of the system. Since the components and parts that have been collected through EPRD/NPRD. The predicted reliability to meet mission requirements and operating conditions is estimated as 4375.9hr. To accomplish the target reliability, the components and parts with high RPN have been identified and changed by analyzing the potential failure modes and effects. By changing the configuration design of components and parts with high-risk, the design is satisfied target reliability.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.79-93
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• 2008

The aim of this work is to generalize reliability equivalence techniques to apply them to a system consists of two independent and non-identical components connected in series(parallel) system, that have constant failure rates. We shall improve the system by using one component only. We start by establishing two different types of reliability equivalence factors, the survival reliability equivalence (SRE) and mean reliability equivalence (MRE) factors. Our second studies, introducing some applications for our studies in airports and our life. Also, we introduced some numerical results.

• 동력기계공학회지
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• 제14권6호
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• pp.61-66
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• 2010

Engineering asset management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting time-to-failure and the probability of failure in future time are essential. In general reliability models, lifetime of component and system is estimated using failure time data. This paper deals with the reliability assessment of elevators using life of main components. Especially this work is concerned with the stochastic nature of life of elevator components. First, we investigate the Weibull statistical analysis of lifetime data for the components. The final goal is to establish the mathematical model for reliability assessment. This work provides more perspectives to future research in the fields of reliability and maintainability.

• 한국신뢰성학회지:신뢰성응용연구
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• 제12권3호
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• pp.187-199
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• 2012

In general, accelerated life testing is performed to reduce testing time. But it is difficult to apply accelerated life testing to a system besides components. This paper developed a software which estimates reliability measures of the system from results of accelerated life testing of components building the system. This software can handle the system with a large number of components and complex topology. Multiple failure modes of a component were also considered in this software. Based on the software, reliability measures of a gearbox example at several conditions were estimated from the accelerated life testing results of three components of the gearbox.

• Structural Engineering and Mechanics
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• 제6권8호
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• pp.901-919
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• 1998

Most reliability-based analyses focus on the reliability of the individual components of a structure. There are many advantages to examining the components in combination as an entire structural system. This paper illustrates an algorithm used in the computer program RELSYS (RELiability of SYStems) which computes the system reliability of any structure which can be modeled as a series-parallel combination of its components. A first-order method is used to initially compute the reliability of each individual component. The system reliability is computed by successively reducing the series and parallel systems until the system has been simplified to a single equivalent component. Equivalent alpha vectors are used to account for the correlation between failure modes during the system reduction process.

• International Journal of Reliability and Applications
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• 제9권2호
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• pp.153-165
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• 2008

This paper discusses the reliability equivalences of a series-parallel system. The system components are assumed to be independent and identical. The failure rates of the system components are functions of time and follow Weibull distribution. Three different methods are used to improve the given system reliability. The reliability equivalence factor is obtained using the reliability function. The fractiles of the original and improved systems are also obtained. Numerical example is presented to interpret how to utilize the obtained results.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2004년도 정기학술대회
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• pp.183-190
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• 2004

Found important components in terms of frequency in the assembly (but not in terms of money). Component 39*** was most important. The failure mode was N69(no light on warning signal the cause of the failure was C15(bad connection). Formed a population for each component. Performed reliability and warranty cost analyses At the component level. At the subsystem level. At the system level. **They don'l trust the warranty cost analysis.** Reliability improvement. Among all the subsystems front \ulcorner subsystem is most vulnerable (among other things due to the large number of components in it), especially components 39*** and 28***.