• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.1-16
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• 2011

In this paper, we proposed the Failure-Diagnostic Context-awareness Middleware (FDCM) for improving the reliability in the USN application service. The middleware diagnoses the failure occurred in sensors or facilities in the indoor USN application system. The new middleware suggested in this paper consists of DataManagement module, ContextProvider module, Contextlnterpreter module, ServiceProvider module and DataStorage module. By analysing the data obtained by the interaction between modules through the diagnostic algorithm, the FDCM determines the malfunction of sensors and equipment devices. Then we verified the performance of middleware by using simulation. As a result, the FDCM showed the high performance in the large systems that many of the sensors and devices are installed.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.189.1-189.1
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• 2012

자장센서는 인공위성에 장착되어 궤도환경에서의 지자기장을 측정하는 센서로서 위성체의 자세결정과 자세제어 등에 활용된다. 일반적으로 자장센서는 원리와 응용범위에 따라 그 종류가 광범위하다. 응용되는 자기현상적으로 분류하면 Faraday 전자기 유도법칙을 이용한 방식, Hall Effect를 이용한 방식, 감지코일의 인덕턴스 변화와 와전류효과를 이용한 방식, 자속분포의 변화에 의한 유도기전력의 변화를 이용한 방식, 자기저항 변화효과를 이용한 방식 등이 있다. 그 중에서도 Faraday's Law를 이용하는 Fluxgate 자장센서가 구조가 비교적 간단하고 경량이며, 높은 신뢰성과 안정성을 가진다. 실제 위성을 발사하기전 지상에서는 위성체를 조립하고 전자파, 진동, 열진공 등과 같은 다양한 환경시험을 수행하는데, 이때 각 환경시험 수행을 전후로 자장센서의 극성시험, 응답시험 등과 같은 기능시험을 수행한다. 본 논문은 다양한 환경시험을 통해 수행한 Fluxgate 자장센서 기능시험 데이터에 대한 추이를 분석하여 위성 발사전 지상에서의 자장센서의 상태와 건전성을 진단하는 방법에 대하여 소개한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.321-324
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• 2013

디젤엔진에서 노킹 현상은 엔진의 성능 및 수명에 직접적인 영향을 주는 중요한 인자이며, 엔진 밸런스 또한 엔진 출력 및 내구성에 직접적인 영향을 미치는 중요한 인자이다. 따라서 노킹 진단 및 엔진 밸런스 보정 알고리즘을 적용하여 ECU를 최적화 하고자 한다. 또한 OBD-II 표준을 사용하여 차량 위주의 진단기를 개발하여 운전자 중심의 진단 서비스를 제공하며, 자동차 고장진단 신호 및 센서 출력 신호를 실시간 통신이 제공 될 수 있게 한다. 이를 위해 자동차 고장진단 신호 및 센서출력 신호를 유선시스템과 무선 시스템인 블루투스 모듈을 이용하여 실시간 통신이 제공 될 수 있는 OBD-II 진단기 S/W를 설계 및 구현하였다.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.38-46
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• 2024

The city gas pipeline network is characterized by being widely distributed and hierarchically connected in a complex manner over a wide area. In order to monitor the status of the widely distributed network pressures with high precision, Multi-block PCA(MBPCA) is recommended. However, while MBPCA has excellent performance in identifying faulty sensors as the number of sensors increases, the fault detection performance deteriorates, and also there is a problem that the model needs to be updated entirely even if minor changes occur. In this study, we developed fault detectability index and fault identificability index to determine the effectiveness of MBPCA application block by block. Based on these indices, we distinguished MBPCA and PCA blocks and developed a fault detection and diagnostic system for the city gas pipeline network of Haean Energy Co., Ltd., and were able to solve the problems that arise when there are many sensors.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.29-35
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• 2019

As the fourth industrial revolution is emerging, many companies are increasingly interested in smart factories and the importance of sensors is being emphasized. In the case that sensors for collecting sensing data fail, the plant could not be optimized and further it could not be operated properly, which may incur a financial loss. For this purpose, it is necessary to diagnose the status of sensors to prevent sensor' fault. In the paper, we propose a scheme to diagnose digital-sensor' fault by analyzing the rising time and falling time of digital sensors through the LSTM(Long Short Term Memory) of Deep Learning RNN algorithm. Experimental results of the proposed scheme are compared with those of rule-based fault diagnosis algorithm in terms of AUC(Area Under the Curve) of accuracy and ROC(Receiver Operating Characteristic) curve. Experimental results show that the proposed system has better and more stable performance than the rule-based fault diagnosis algorithm.

• The Monthly Diabetes
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• s.288
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• pp.58-63
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• 2013

센서의 활용 분야를 헬스케어가 이루어지는 주요 장소와 주체, 질병의 특성 등을 고려하여 예방, 진단, 모니터링의 3단계로 구분하여 센서의 으용을 설명한다. u-Health 기술 내용중 네 번째로 센서의 헬스케어 응용분야를 알아본다.