• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1346-1352
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• 2009

Warranty cost of automobile parts varies depending on the parts failure rate in a warranty region of individual markets. Parts failure rate is significantly affected by usage-rate given that other stressors of individual markets are similar. Accordingly, warranty cost can be predicted by failure modeling which reflects usage-rate and using a stochastic process. In this paper, one-dimensional approach is used by applying accelerated failure time model on the assumption that the usage-rate is linear. Such model can explain changes in parts failure rate depending on the changes in usage-rate since it can be expressed as a function of usage-rate. Therefore, acquisition of usage-rate in a new market will automatically lead to estimate of failure rate even without warranty data and warranty cost of parts can be predicted through a renewal process in replacement cases. A case study using warranty data of two real markets is presented in the application part of this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.432-436
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• 2016

The cooler, which is the main part in a Thermal Observation Device (TOD), makes the TOD function by reducing the temperature. As the cooler is imported, overseas enterprises presented 20,000 hours as the operation time and the military have used the cooler as presented. However, failures have occurred occasionally after mass production stage. Therefore, we need to analyze the MTBF of the TOD cooler. So, military and defense industry companies collected the failure data of the TOD cooler. We analyze the MTBF of the TOD cooler using survival probability function and failure data. We find the optimal distribution by applying parametric method and estimate parameters. We determine that the Log-logistic distribution is the most appropriate for this data. Also, we analyze the reliability per hour of the TOD cooler. The result of MTBF of the TOD cooler was higher than that of presented by oversee enterprises.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.119-121
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• 2005

전력케이블, 특히 지중 송전케이블은 사고발생시 그 파급효과가 크기 때문에 빠르고 정확한 고장점의 탐지가 필요하다. 본 논문에서는 지중 케이블의 고장 위치를 파악하기 위해 필요한 신호를 계측하고 이를 빠른 시간 내에 저장하여 사고 발생 후 이를 분석하여 고장점을 찾을 수 있도록 하는 고장점 탐지 장치를 위한 회로 설계에 대하여 언급한다. 케이블의 고장점 탐색 기술 개발을 위해서는 고장시에 발생하는 과도현상을 기록할 수 있도록 회로를 설계해야 하는 바 센서 구성과 입력 회로반의 설계, 데이터 저장 및 분석을 위한 회로의 설계는 고장점 탐색 장치 개발에 있어 필수적인 기술이다. 개발된 지중 송전선 고장점 탐색을 위한 측정회로는 사고지점 계산을 위해 필요한 신호측정에 효과적이며 실제 전력 공급 계통에 손쉽게 설치할 수 있는 장점을 가지고 있다. 이하에 지중송전선 고장점 탐색을 위한 측정 회로 설계에 관하여 언급한다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.109-111
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• 2007

전력거래소(KPX)는 전국에 산재된 발 변전소로부터 2초 혹은 4초 주기로 계통운영에 필요한 아날로그, 디지털 데이터를 실시간으로 취득하고 있다. 이렇게 취득한 데이터는 EMS 전력계통 감시 및 제어를 위한 각종 화면, 자동발전제어/경제급전(AGC/ED), 상태추정(SE), 상정고장해석(CA)등 실시간 계통운영과 조류계산(PF), 고장계산(SCT), STNET(PF, CA), 상정고장해석 등 검토모드로 급전소 휴전업무 검토에 활용되고 있다. 또한 EMS에서 취득한 실시간 데이터를 MOS(시장 운영시스템)로 보내어 상태추정을 추행하고 이 값을 기반으로 발전기별 경제적인 출력 값인 FMD(5분 급전지시)를 EMS로 보내어 EMS의 주요기능인 AGC(자동발전제어)로 경제적이고 안정된 출력 조정을 하고 있다. 이러한 이유로 EMS 및 MOS의 상태추정 견과의 정확도와 실행 안정성은 매우 중요하다고 하겠다. 본고에서는 EMS 계통해석기능 검증 결과와 향후 데이터 정확도 개선은 위한 과정의 일환으로 EMS의 상태추정과 MOS의 상태추정의 상호비교 결과를 소개하고자 한다.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.45-50
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• 2019

This study proposes a method to measure software reliability according to software reliability measurement model to measure software reliability. The model presented in this study uses the distribution of Non - Homogeneous Poisson Process and presents a measure of the software reliability of the presented model. As a method to select a suitable software reliability growth model according to the presented model, we have studied a method of proposing an appropriate software reliability function by calculating the mean square error according to the estimated value of the reliability function according to the software failure data. In this study, we propose a reliability function to measure the software quality and suggest a method to select the software reliability function from the viewpoint of minimizing the error of the estimation value by applying the failure data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• ETRI Journal
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• v.11 no.4
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• pp.3-21
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• 1989

본고에서는 TDX 시스팀의 신뢰도 보증 및 효율적 관리를 위하여 TDX-1A 시스팀에서 사용되고 있는 부품의 신뢰도 예측치와 현장운용데이터에서 나온 신뢰도 평가치를 비교 검토한후, TDX-1A 시스팀의 신뢰도 예측결과를 검증하고, 또한 외국의 고장율 예측방법과의 차이점을 도출하여 이를 TDX 시스팀의 신뢰도 예측방법에 적용한 후 TBX-1A 현장운용데이터로 보완된 TDX 환경에 적합한 부품 고장율 예측방법을 정립, 제시하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.319-321
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• 2002

인터넷 기반 네트워크 통신의 형태가 단순한 텍스트 위주의 데이터로부터 실시간 환경을 요구하는 멀티미디어 데이터 형태로 변하고 있으며, 실시간 분산처리 시스템에 대한 수요가 증가하고 있다. 이러한 실시간 시스템은 분산 실시간 처리 시스템에 대한 전반적인 분야의 발전이 필요하며, 특히 그 중에서도 신뢰성이 높은 그룹 통신 시스템의 개발은 매우 중요하다. 따라서 본 논문에서는 고장탐지 기능이 있는 실시간 그룹 통신 시스템을 설계하였다. 설계된 실시간 그룹통신 시스템은 이미 TMO 모델을 통하여 메시지 신뢰성과 고장탐지에 관한 시뮬레이션을 하였고, 그 결과를 분석하여 실시간 그룹통신 시스템 설계에 반영하였다.