• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.183-193
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• 2008

This paper shows the results of failure mode analysis and the system-level reliability model for the flight test of KSLV-I upper stage. First, the critical 14 functions of KSLV-I upper stage are identified and the mission profile of the flight test is analyzed. Then, based on the functional analysis and the mission profile analysis, we construct a hierarchical structure of failure modes and a system-level reliability model for the flight test of KSLV-I upper stage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.2
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• pp.70-76
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• 1984

In this paper, a functional m-redundant system, which is m-fault tolerant, is defined based on the graph-theory. This system is designed to be t(t$\geq$m) fault-diagnosable by comparing its unit's outcomes without additive test functions, so, the system down for diagnosis is not needed. The diagnostic model for this system is presented. It is to avail the redundancy of the system effectively. It is shown that this model can be converted into Preparata's model. Thus, the diagnostic characteristics of a functional m-redundant system is analyzed by the method originated by Preparata et al.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.102-110
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• 2002

An effective scheme to detect and diagnose multiple failures in a dynamic system is described for the case where the measurement noise is correlated sequentially in time. It is based on the modified interacting multiple model (MIMM) estimation algorithm in which a generalized decorrelation process is developed by employing the autoregressive (AR) model for the colored noise and applying measurement difference method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1232-1235
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• 2022

최근 자동차, 철도차량 등 사용자가 있는 기계 시스템에서의 고장 발생 시 사용자의 안전과 관련된 사고로 이어질 수 있어 부품에 대한 모니터링 및 고장 여부 판단은 매우 중요하다. 이러한 부품 중에서 베어링은 회전체와 회전하지 않는 물체 사이에서 회전이 원활하게 이루어질 수 있도록 하는 부품인데, 베어링에 결함이 발생하게 될 경우, 기계 시스템이 정지하거나, 마찰 열에 의해 화재 등의 치명적인 위험이 발생한다. 본 논문에서는 Resnet과 오토인코더를 활용하여 진동 신호 기반의 베어링의 고장을 감지하고 분류할 수 있는 모델을 제안한다. 제안 방법은 raw data를 이미지로 변환하여 입력으로 사용하는데, 이러한 접근을 통해 수집된 데이터의 손실을 최소화하고 데이터가 가지는 정보를 최대한 분석에 활용할 수 있다. 제안 모델의 검증을 위하여 공개된 데이터셋으로 학습/검증 하였고, 제안 방법이 기존 방법과 비교하여 더 높은 F1 Score와 정확도를 보임을 확인하였다.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.123-128
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• 2004

본 논문에서는 가속 수명 시험을 통하여 열전소자의 수명 분포, 모수 등을 규명하였으며 고장 분석을 통하여 열전 소자의 수명 증가를 위한 대책 방안을 논의하였다. 가속 수명 시험 결과 열전 소자는 형상 모수 3,6인 Weibull 분포를 따름을 알 수 있었다. 열전 소자가 반도체 부품임에도 불구하고 형상 모수가 큰 이유는 반복 Bending에 의한 피로 파괴가 발생하기 때문임을 고장 분석을 통하여 규명하였다. 위의 고장 메커니즘을 설명할 수 있는 가속 모델식은 Coffin-Manson식으로 설명되어 질 수 있으며 가속수명시험 결과 재료 상수는 1.8임을 알 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1B
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• pp.50-58
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• 2012

The objective of this study is to develop the theoretical model of the telecommunication system service availability from the user perspective. We assume non-homogeneous Poisson process for the call arrival process and continuous time Markov chain for the system state. The proposed model effectively describes the user model of the user-perceived service reliability by including the time-varying call arrival rate. We also include the operational failure state where the user cannot receive any service even though the system is functioning.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.23-31
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• 2022

This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.936-943
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• 2009

In this study, a risk assessment model based on the ETA (Event Tree Analysis) and FTA (Fault Tree Analysis) is developed according to the procedure of hazard analysis and risk assessment in order to estimate the risk quantitatively. The FTA technique is applied to estimate the branch probability (frequency) and the ETA technique is applied to estimate the consequence for each branch path on the ET (Event Tree). A risk assessment model is developed by the combination of those ETA and FTA. In addition, the reliability and the validity of the risk assessment model are verified by comparing the risk estimated through the developed model with the actual equivalent fatality.