• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.180-182
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• 2001

기존의 디지털 계전기 교육/훈련 방법은 적절한 교육용 소프트웨어 및 기기의 부재로 인해 교육을 받는 훈련생들을 단기간 내에 이해시키는데 어려움이 있었다. 본 연구의 목적은 GUI 환경에서 다양한 모의 고장 발생, 모의 고장 시나리오의 입력, 편집 수정, 삭제가 가능하고 디지털 계전기의 교육/훈련 기능과 함께 훈련 결과의 평가와 훈련일지의 작성 기능을 포함하는 디지털 계전기 시뮬레이터의 개발이다. 본 논문에서는 고장종류, 고장발생 각, 고장거리, 데이터의 샘플수에 따라 EMTP를 이용하여 모의한 후, 디지털 신호 처리를 이용하여 직류 성분과 고조파 성분을 제거하고, 기본파 성분을 추출한다. 그리고 추출된 데이터를 사용하여 임피던스 궤적을 도시하고. 그 결과를 사용자에게 보여주도록 하였다. 특히, GUI 환경으로 구성하여 알고리즘의 수행과정을 보다 쉽게 이해하고. 사용할 수 있도록 함으로써, 교육/훈련의 효과가 극대화되도록 하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.212-213
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• 2021

자율운항선박 기술개발사업 중 2세부(자율운항선박 핵심 기관시스템 성능 모니터링 및 고장예측 진단 기술 개발) 과제는 자율운항선박의 추진 및 전력 생산을 담당하는 핵심 기관시스템의 운전 상태를 실시간 모니터링하여, 계측 데이터 기반의 고장 진단/예측을 수행하고 장애 발생 시 원격 지원체계를 통해 체계적/전문적 정비를 수행할 수 있도록 지원하는 기술이다. 자율운항선박은 선원이 없이 자율적으로 운항도 하지만, 핵심장비/기자재에 대해서도 실측 데이터를 기반으로 스스로 판단하여 고장여부에 대한 의사결정이 가능하여야 한다. 선박 기관시스템은 선박 운항의 안전과 정시 입·출항에 핵심이 되는 장비/기자재로써 자율운항선박 구현에 필요한 핵심 기술이다. 본 연구에서는 자율운항선박 핵심장비 중 보조기기 2종(Pump, Purifier), 배관(Seawater Pipe, Steam Pipe)의 성능 모니터링 및 고장예측/진단 소프트웨어를 개발하기 위한 연구를 수행한다.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.595-601
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• 2017

Due to the electrification of railways, fault at the traction line is increasing year by year. So importance of the fault locator is growing higher. Nevertheless at the field traction line, it is difficult to locate accurate fault point due to various conditions. In this paper railway feeding system current loop equation was simplified and generalized though measured data. And substation, train power data were measured under synchronized condition. Finally catenary impedance was predicted through generalized equation. Also simulation model was designed to figure out the effect of load current for train at same location. Train current was changed from min to max range and catenary impedance was compared at same location. Finally, power measurement was performed in the field at train and substation simultaneously and catenary system impedance was predicted and calculated. Through this method catenary impedance can be measured more easily and continuously compared to the past method.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.3
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• pp.167-178
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• 2010

To diagnose a stator winding fault caused by a short-circuited turn in a permanent magnet synchronous motor (PMSM), an on-line based fault detecting scheme during motor operation is presented. The proposed scheme is based on monitoring the second-order harmonic components in q-axis current obtained through the harmonic analysis and a winding fault is detected by comparing these components with those in normal conditions. The linear interpolation method is employed to determine harmonic data in arbitrary normal operating conditions. To verify the effectiveness of the proposed fault detecting scheme, a test motor to allow inter-turn short in the stator winding has been built. The entire control system including harmonic analysis algorithm and fault detecting algorithm is implemented using DSP TMS320F28335. The proposed scheme does not require any additional hardware and can effectively detect a fault during motor operation so long as the steady-state condition is satisfied.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of KIISE
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• v.42 no.12
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• pp.1551-1560
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• 2015

Software Reliability Growth Models (SRGMs) are used to quantitatively evaluate software reliability and to determine the software release date or additional testing efforts using software failure data. Because a single SRGM is not universally applicable to all kinds of software, the selection of an optimal SRGM suitable to a specific case has been an important issue. The existing methods for SRGM selection assess the goodness-of-fit of the SRGM in terms of the collected failure data but do not consider the accuracy of future failure predictions. In this paper, we propose a method for selecting SRGMs using the trend of failure data and failure prediction ability. To justify our approach, we identify problems associated with the existing SRGM selection methods through experiments and show that our method for selecting SRGMs is superior to the existing methods with respect to the accuracy of future failure prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1004-1011
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• 2006

In this study, we have performed a systematic case study on troubles and diagnoses of passenger car's engine based on OBD. We have acquired 1,242 data in order to analysis accurate troubles' causes and apposite diagnoses. 128 data of them are got using OBD apparatus, and the rest of them are collected on related website. As results, distribution on trouble cases shows bad idling(32%), poor acceleration(21%), stop in running(19%), faulty start(11%), inferior fuel economy(9%), and insufficient power(8%) in order of magnitude. And in the systematic cases, it is not difficult to detect troubles in a single part. But we know that special apparatus such as multichannel scanner is needed in complicated troubles. Furthermore we think that the survey is continued in various ways for more systematic case study on troubles and diagnoses.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.