• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.631-638
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• 2015

In this study, semiquantitative failure mode, effects, and criticality analysis (FMECA) for the reliability analysis of a solid rocket propulsion system is performed. The semiquantitative FMECA is composed of failure mode and effects analysis (FMEA) and criticality analysis (CA). To perform FMECA, the structure of the solid rocket propulsion system is divided into 43 parts down to the component level, and FMEA is conducted at the design stage considering 137 potential failure modes. CA is then conducted for each failure mode, during which the criticality number is estimated using the failure rate databases. The results demonstrate the relationship between potential failure modes, causes, and effects, and their risk priorities are evaluated qualitatively. Additionally, several failure modes with higher criticality and severity values are selected for high-priority improvement.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1394-1400
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• 2008

This study investigates the Failure Modes, Effects and Criticality Analysis (FMECA) Method for the railroad vehicle. Recently, RAMS (Reliability, Availability, Maintainability and Safety) is one of the most important issues in the railroad industry. FMECA is prerequisite for the RAMS Analysis, and it is a procedure to identify the potential failure modes and their effects and to reduce or mitigate the critical effects on the system. FMECA is used in various industries and it is specialized in each industry. For instance, MIL-1629a and SAE-J1739 are specialized FMECA method for Military industry and Automotive industry, respectively. Although the railroad industry requires the high reliability system, it does not have a specialized FMECA yet. Thus, in this paper, an FMECA method specialized to the railroad vehicle was proposed through analyses and comparison of the MIL-1629a, SAE-J1739 and IEC-60812 standards.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3370-3377
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• 2015

FMECA(Failure Mode, Effects and Criticality Analysis) techniques to make quantitative evaluation of failure effects severity and criticality have been applied to systematic failure analysis for reliability improvement of train which should provide regular service and secure high level of safety as a mass transportation system. These FMECA techniques do not fully reflect the inherent train operation and maintenance circumstances because they are based on the FMECA standards devised for other industries such as automobile industry and FMECA standard dedicated to train industry has not been established yet. This paper analyzes FMECA standards for various industries, and suggests a FMECA technique dedicated to train industry which makes failure effect analysis and criticality analysis step by step and makes criticality analysis placing emphasis on the severity of the failure effect. The proposed technique is applied to FMECA of high-speed current breaker which is a core safety device of train using field failure data for 15 years of train maintenance. The FMECA results show that breakage of arc chute has the highest risk with 3rd severity class and 5th criticality class among all the components of high-speed circuit breaker. Damage and poor contact of electronic valve, and cylinder breakage with 3rd severity class and 4th criticality class are followed by. These results can be applied to improvement of design and maintenance process for high-speed circuit breaker of train.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.565-578
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• 2016

A probabilistic based systems approach is addressed in this study for the reliability prediction of solid rocket motors (SRM). To achieve this goal, quantitative Failure Modes, Effects and Criticality Analysis (FMECA) approach is employed to determine the reliability of components, which are integrated into the Fault Tree Analysis (FTA) to obtain the system reliability. The quantitative FMECA is implemented by burden and capability approach when they are available. Otherwise, the semi-quantitative FMECA is taken using the failure rate handbook. Among the many failure modes in the SRM, four most important problems are chosen to illustrate the burden and capability approach, which are the rupture, fracture of the case, and leak due to the jointed bolt and O-ring seal failure. Four algorithms are employed to determine the failure probability of these problems, and compared with those by the Monte Carlo Simulation as well as the commercial code NESSUS for verification. Overall, the study offers a comprehensive treatment of the reliability practice for the SRM development, and may be useful across the wide range of propulsion systems in the aerospace community.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.254-259
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• 2007

PM(Preventive Maintenance) can avail the generating unit to reduce cost and gain more profit in a competitive supply-side power market. So, it is necessary to perform reliability analysis on the power systems in which reliability is essential. Thus, to schedule optimal PM planning based on reliability that is defined RCM(Reliability-Centered Maintenance), FMECA(Failure Mode Effects and Criticality Analysis) assessment is very important. Therefore, in this paper, the procedure of FMECA assessment for optimal RCM is proposed by probabilistic approach using real historical failure data of combustion-turbine generators in Korean power systems. The stochastic FMECA is performed based on the effects of probable failure modes of combustion-turbine generating unit.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1529-1532
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• 2007

Failure Mode, Effects, and Criticality Analysis (FMECA) is an extension of FMEA which includes a criticality analysis. The criticality analysis is used to chart the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. However, there are several limitations. Measuring severity of failure consequences is subjective and linguistic. Since The result of FMECA only gives qualitative and quantitative informations, it should be re-analysed to prioritize critical units. Fuzzy set theory has been introduced by Lotfi A. Zadeh (1965). It has extended the classical set theory dramatically. Based on fuzzy set theory, fuzzy logic has been developed employing human reasoning process. IF-THEN fuzzy rule based assessment approach can model the expert's decision logic appropriately. Fault tree analysis (FTA) is one of most common fault modeling techniques. It is widely used in many fields practically. In this paper, a simple fault tree analysis is proposed to measure the severity of components. Fuzzy rule based assessment method interprets linguistic variables for determination of critical unit priorities. An rail-way transforming system is analysed to describe the proposed method.

• Journal of the Korean Society for Railway
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• v.12 no.3
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• pp.342-347
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• 2009

Failure Mode Effects and Criticality Analysis (FMECA) is one of most widely used methods in modern engineering system to investigate potential failure modes and its severity upon the system. While performing FMECA, the experts evaluates criticality and severity of each failure mode and visualize the risk level matrix putting those indices to column and row variable respectably. Which results uncertainty in the result. In order to handle the uncertainty and conclude risk level matrix, this paper proposes a new FMECA procedure using minimal cut set (MCS) and fuzzy theory. Severity is calculated by proposed structural importance while criticality is determined by typical equipment failure rate data from IEEE Std 493. Finally, the risk level is compounded of these indices.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.26-34
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• 2016

In this study, reliability prediction of the ignition system of hybrid rocket is performed. The FMECA is preceded to the reliability prediction. To this end, the ignition system is divided into 5 components and 19 potential failure modes. The failure cause and effects are identified and criticality analysis is carried out for each failure mode, in which the criticality number is estimated using the failure rate databases. Among the numbers, the failure modes and components with higher criticality and severity are chosen and allocated with higher weighting factor. The reliability predictions are performed using the failure rate databases, from which the current ignition system is found to satisfy the target reliability.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.148-153
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• 2009

Failure Mode Effects and Criticality Analysis (FMECA) is one of most widely used methods in modem engineering system to investigate potential failure modes and its severity upon the system. FMECA evaluates criticality and severity of each failure mode and visualize the risk level matrix putting those indices to column and row variable respectably. Generally, those indices are determined subjectively by experts and operators. However, this process has no choice but to include uncertainty. In this paper, a method for eliciting expert opinions considering its uncertainty is proposed to evaluate the criticality and severity. In addition, a fuzzy expert system is constructed in order to determine the crisp value of risk level for each failure mode. Finally, an illustrative example system is analyzed in the case study. The results are worth considering while deciding the proper policies for each component of the system.