• 한국신뢰성학회지:신뢰성응용연구
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• 제12권1호
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• pp.13-24
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• 2012

This paper applied the Blue Print and FMEA (Failure Mode and Effect Analysis) to education service in order to raise the education service satisfaction. First, the Blue Print is deployed to come up with strategies to overcome the fail possibility point and waiting point. Next, in order to analyze the fail factors and alternative strategies, the Blue Print of education service is applied to FMEA. The results are as follows; first, the ommission from information document by web-mail or e-mail, Second, thing that selected in spite of company uneducated, thing that omitted despite the company is target, and the unsatisfaction of attendee about training contents. Third, the delay of counsel at the telephone reply, erroneous list of course name and attendee at HRD (Human Resource Development), omission of check whether attends or not. Except for unsatisfaction of attendee, all appears at the process that service delivered. And the unsatisfaction of attendee is about education contents. Both is the factor which have influence on the education service quality. The strategies to remove the failure mode are training and manual development on service and work, a thorough management and check of information system like as ERP (Enterprise Resoure Planning), HRD, education institution list DB (Data Base), on-line application system, a development of education program to offer best education that reflect the user needs and continuously changing environment.

• 산업경영시스템학회지
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• 제23권61호
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• pp.75-87
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• 2000

This paper proposes a procedure for designing an accelerated test using SMAT(Stress, (failure) Mechanism and Test) model describing the relation among stress, failure mode/mechanism and test method. In SMAT model the stresses to be applied are derived from the environmental factor analysis, the relative importance of those stresses can be estimated using AHP(Analytic Hierarchy Process) and failure mode/mechanism and test method are derived from the fields failure information and FMEA(Failure Mode and Effect Analysis). By applying the procedure we can make a selection of major factors to cause the failure of assembly and design the accelerated test using DOE(Design of Experiments) The procedure is illustrated with an qualification test case study of washing machine shaft assembly in "A" electric appliance company.

• 벤처창업연구
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• 제9권1호
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• pp.177-186
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• 2014

FMEA는 실패로 인한 위험을 최소화하기위해 실패의 요인과 그로인한 영향을 사전에 평가하는 체계적인 방법이다. 이 방법은 제품의 신뢰도 문제를 해결하기 위해 제조산업 분야에서 주로 사용되어 왔으나, 서비스의 역할과 중요성이 커지면서 최근에는 이를 서비스의 신뢰도 문제에도 사용하고 있다. 하지만, 서비스에서는 고객이 서비스 전달 프로세스에 참여하며 고객마다의 이질성 등으로 인해 제조업을 위해 개발된 FMEA를 직접 사용할 수은 없다. 이러한 이유로 인해, FMEA를 서비스에 적용하기위한 여러 연구가 이루어지고 있다. 본 논문에서는, 심각도, 발생빈도, 검출력으로 우선순위를 평가하던 기존의 RPN 대신에, 서비스 특성을 고려하여 심각도, 발생빈도, 회복력 3가지로 평가하는 새로운 지수 S-RPN을 제시하였으며, 기존연구의 사례를 통해 제시된 방법의 효용성을 평가하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권2호
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• pp.162-170
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• 2016

Purpose: The purpose of this study was to analyze the failure modes of an auto transfer switch (ATS), determine the most common failure mechanisms, and iterate the design to improve reliability. Methods: We carried out failure mode and effect analysis (FMEA) to determine the failure modes and mechanisms. We identified the parts or modules that required improvement via two-stage quality function deployment based on FMEA, and improvements to reliability were monitored using the Gomperz growth model. Results: The main failure modes of the ATS were damage to, and deformation of, the stator / movable element due to repetitive movements. Five iterations of design modification were carried out, and the mean time to failure (MTTF) increased to 14,539 cycles, corresponding to 85% of the target MTTF. The Gompertz growth model indicates that the 10th iteration of design modification is expected to achieve the target MTTF. Conclusion: We improved the reliability of mechanical parts via failure mode analysis, and characterized the iterative improvements in the MTTF using the Gompertz growth model.

• 한국건축시공학회지
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• 제23권4호
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• pp.417-428
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• 2023

사고 원인에 대한 철저한 분석은 사고 재발 방지를 위한 필수적인 과정이다. 해체공사 사고의 원인을 살펴보면 작업자의 불안전한 행동, 불안전한 상태, 심리적·신체적 상태, 현장관리 원인 등 매우 다양하다. 현재 해체공사 사고통계는 지속적으로 조사·보고되고 있으나 사고 유형에 따른 보다 근본적인 원인 분류 정보가 필요하다. 본 연구에서는 하인리히의 도미노 이론을 바탕으로 해체공사 사고의 유형에 따라 사고원인(불안전한 행동, 불안전한 조건)과 휴먼에러(인적요인)를 분류하였다. 본 연구에서는 해체공사시 사고유형에 따라 사고원인을 체계적으로 분류하기 위해 QFD-FMEA(Quality Function Deployment - Failure Mode Effect Analysis) 3단계 모델을 사용하였다. 사고원인 분류 결과는 사고예방을 위한 안전지식 및 체크리스트로 활용할 수 있다.

• 산업경영시스템학회지
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• 제42권2호
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• pp.28-35
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• 2019

Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.

• 시스템엔지니어링학술지
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• 제18권1호
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• pp.84-89
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• 2022

For system maintenance optimization, it is necessary to establish a state information system by CBM+ including CBM and RCM, and sensor selection for CBM+ application requires system process for function model analysis at the early design stage. The study investigated the contents of CBM and CBM+, analyzed the function analysis tasks and procedures of the system, and thus presented a D-FMEA based sensor selection inference methodology at the early stage of design for CBM+ application, and established it as a D-FMEA based sensor selection inference process. The D-FMEA-based sensor inference methodology and procedure in the early design stage were presented for diesel engine sub assembly.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권3호
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• pp.180-191
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• 2016

Purpose: The purpose of this study was to develop the accelerated life test method for Constant Electrical Potential Electrolysis gas sensor (CEPE gas sensor). Methods: The parts and modules of CEPE gas sensor were analyzed by using Reliability Block Diagram (RBD). Failure Mode and Effect Analysis (FMEA) and Quality Function Deployment (QFD) methods were performed for each part to determine the most affecting stress factor in its life cycle. The long term testing was conducted at three different dry heat levels and the acceleration factor was developed by using Arrhenius relationship. Conclusion: The acceleration factor for CEPE gas sensor was developed by using FMEA, QFD, and statistical analysis for its failure data. Also qualification tests were designed to meet the target life.

• Journal of Advanced Research in Ocean Engineering
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• 제3권3호
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• pp.125-135
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• 2017

The paper considers the practical application of the FMEA(Failure Mode and Effect Analysis) method to assess the operational reliability of the LNG(Liquefied Natural Gas) transfer system, which is a potential problem for the connection between the LNG FPSO and LNG carrier. Hazard Identification (HAZID) and Hazard operability (HAZOP) are applied to identify the risks and hazards during the operation of LNG transfer system. The approach is performed for the FMEA to assess the reliability based on the detection of defects typical to LNG transfer system. FTA and FMEA associated with a probabilistic risk database to the operation scenarios are applied to assess the risk. After providing an outline of the safety assessment procedure for the operational problems of system, safety assessment example is presented, providing details on the fault tree of operational accident, safety assessment, and risk measures.

• 한국기계가공학회지
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• 제19권11호
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.