• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.7-15
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• 2016

The purpose of this paper is to carry out Failure Modes and Effects Analysis (FMEA) and use criticality in order to determine risk priority number of the components of electric power installations in Engineering college building of D university. In risk priority number, GROUP A had 7 failure modes; more specifically, Transfomer had 4 modes, Filter(C)(1 mode), LA(1 mode), and CB(MCCB)(1 mode), and thus 4 components had failure modes. In terms of criticality, high-grade group a total of 16 failure modes, and 7 components-LA(1 mode), CB(MCCB)(1 mode), MOF(2 modes), PT(1 mode), Transformer(7 modes), Cable(3 modes), and Filter(C)(1 mode)-had failure modes. Comparison of risk priority number and criticality was made. The components which had high risk priority number and high criticality were Transformer, Filter(C), LA, and CB(MCCB). The components which had high criticality were MOF and cable. In particular, Transformer(RPN: 4 modes, Criticality: 7 modes) was chosen as an intensive management component.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.1
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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 advanced smart convergence
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• v.1 no.1
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• pp.57-60
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• 2012

In this paper, we have performed a safety analysis on an automotive evaporator inspection system. We performed the bi-directional analysis on the manufacturing line. Software Fault Tree Analysis (SFTA) as backward analysis and Software Failure Modes, Effects, & Criticality Analysis (SFMECA) as forward analysis are performed alternately to detect potential cause-to-effect relations. The analysis results indicate the possibility of searching and summarizing fault patterns for future reusability.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.10-17
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• 2016

The purpose of this paper is to deduct components that are in the group of highest risk(top 10%). the group is conducted for classification into groups by values according to risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet. Top 10% of failure mode among total potential failure modes(72 failure modes) of ESS included 5 BMS(battery included) failure modes, 1 invert failure mode, and 1 cable connectors failure mode in which BMS was highest. This is because ESS is connected to module, try, and lack in the battery part as an assembly of electronic information communication and is managed. BMS is mainly composed of the battery module and communication module. There is a junction box and numerous connectors that connect these two in which failure occurs most in the connector part and module itself. Finally, this paper proposes RPN by each step from the starting step of ESS design to installation and operation. Blackouts and electrical disasters can be prevented beforehand by managing and removing the deducted risk factors in prior.

• 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.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.162-168
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• 2009

FMEA(Failure Mode and Effect Analysis) is a failure analysis method for the system to identify the potential failure modes, and their effects and causes to reduce or mitigate the critical effects of the system. FMEA for railway was introduced with reliability of railway system, and this was used for identifying and analysing the possible hazards qualitatively to meet the requirements in early stage of business. In general, the overall failure data of system could be managed from design stage by FMEA, and also the countermeasures to prevent many predicted failures could be established by identification of failure modes and assement of failure effects by FMEA. Using these advantages of FMEA, the effectiveness of reliability improvement could be expected if FMEA is applied continuously in operation stage. It is essential that railway vehicles are maintained with high level of safety and reliability not to happen any failures in operation. This paper is proposed the proper FMEA for maintenance of railway vehicles compared with existing FMEA.

• 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.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2031-2036
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• 2022

Energetic particle strikes the device and induces data corruption in the configuration memory (CRAM), causing errors and even malfunctions in a system on chip (SoC). Software-based fault injection is a convenient way to assess device performance. In this paper, dynamic partial reconfiguration (DPR) is adopted to make fault injection on a Xilinx 16 nm FinFET Ultrascale+ MPSoC. And the reconfiguration module implements the Sobel and Gaussian image filtering, respectively. Fault injections are executed on the static and reconfiguration modules' bitstreams, respectively. Another contribution is that the failure modes and effects analysis (FMEA) method is applied to evaluate the system reliability, according to the obtained injection results. This paper proposes a software-based solution to estimate programmable device vulnerability.

• Journal of Applied Reliability
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• v.17 no.1
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• pp.11-21
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• 2017

Purpose: The occurrence ranks of failure modes can come from the real failure but the severity ranks of failure modes require a highly subjective point of view of users. The severity ranks have to find more objective and scientific values. Methods: We found the optimal values by using the correlation analysis between failure mode effects and the criticality number like RPN (Risk Priority Number) in RCM. Result: This paper shows the result that verified whether the weighted values on each failure effect in criticality number calculation is suitable to the actual failures or not. To get the verification, it used the 5 year data and correlation analysis. Based on the analyzed result, We proposed the more suitable values. Conclusion: This correlation analysis approach can provide guidance of RCM analysis across many industries and situations.