• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.36-41
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. FDD algorithm can detect refrigerant leak failure, when 20% amount of charged refrigerant for normal operation leaks from the water chiller. The refrigerant leak failure caused COP reduction by 6.7% compared with normal operation performance. When two kinds of faults, such as a decrease in the mass flow rate of cooling water and temperature sensor fault of cooling water inlet, are detected, COP is a little decreased by these faults.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.86.5-86
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• 2002

The measurement sensor may contain unknown bias in addition to the white noise in the measurement sequence. In this paper, fault detection and diagnosis scheme for the measurement sensor is developed based on the adaptive estimator. The proposed scheme consists of a parallel bank of Kalman-type filters each matched to a set of different possible biases, a mode probability evaluator, an estimate combiner at the outputs of the filters, a bias estimator, and a fault detection and diagnosis logic. Monte Carlo simulations for the PWR steam generator in the nuclear power plant are provided to illustrate the effectiveness of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4123-4141
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• 2019

Autonomous driving technology is divided into 0~5 levels. Of these, Level 5 is a fully autonomous vehicle that does not require a person to drive at all. The automobile industry has been trying to develop Level 5 to satisfy safety, but commercialization has not yet been achieved. In order to commercialize autonomous unmanned vehicles, there are several problems to be solved for driving safety. To solve one of these, this paper proposes 'A Deep Learning Part-diagnosis Platform(DLPP) based on an In-vehicle On-board gateway for an Autonomous Vehicle' that diagnoses not only the parts of a vehicle and the sensors belonging to the parts, but also the influence upon other parts when a certain fault happens. The DLPP consists of an In-vehicle On-board gateway(IOG) and a Part Self-diagnosis Module(PSM). Though an existing vehicle gateway was used for the translation of messages happening in a vehicle, the IOG not only has the translation function of an existing gateway but also judges whether a fault happened in a sensor or parts by using a Loopback. The payloads which are used to judge a sensor as normal in the IOG is transferred to the PSM for self-diagnosis. The Part Self-diagnosis Module(PSM) diagnoses parts itself by using the payloads transferred from the IOG. Because the PSM is designed based on an LSTM algorithm, it diagnoses a vehicle's fault by considering the correlation between previous diagnosis result and current measured parts data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2195-2196
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server. Fourth one was device server. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer. In main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.563-564
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server Fourth one was device solver. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer. In main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this Property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1229-1230
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server. Fourth one was device server. Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer in main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 C
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• pp.1689-1690
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• 2006

This study is for implementation of PC based Motor fault diagnosis system. By using harmonics and current signals of the motor, this system diagnoses the motor condition by accumulated harmonic contribution rate. In this proposed system that was composed of 5 parts. A sensor, connection box, evaluation board, device server, and main computer are those. There were two types of sensor, one was harmonic sensor the other was current sensors. The signal was acquired by sensor, and transferred to evaluation board. Second one is connection box. Because the output type of sensor and input type of evaluation board is different, connection box was necessary. Third one was evaluation board. The signal from the sensor was converted to digital signal in evaluation board. And this signal was transferred to device server. Fourth one was device server Device server transferred the data from evaluation board to main computer. And the last one was other parts controlled by main computer. In main computer, there were communication and diagnosis algorithms. The result was derived by main computer. In the result, there were 12 categories and 5 levels of motor conditions. The proposed system had some advantages comparing with stand alone type commercial motor fault diagnosis system. The first, by using remote access it was easier to get the conditions of motor. The second, there was no need to handle the sensors when users measured the motor signals. By this property, no one was necessary at motor location site. The third, this system was less restricted by times and places than commercial stand alone type diagnosis system. Therefore users can operate this system only using the main computer. Once the sensors are installed at the motor, users doesn't need to move to check up the condition of motors. Moreover, if there is ethernet hub, many motors can be not only diagnosed at once but also decreased its cost.

• Transactions on Control, Automation and Systems Engineering
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• 제4권4호
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• pp.270-276
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• 2002

An integrated robust fault diagnosis and fault accommodation strategy for a class of linear stochastic systems subjected to unknown disturbances is presented under the assumption that only a single fault may occur at a given time. The strategy is based on the fault isolation and estimation using a bank of robust two-stage Kalman filters and introduction of the additive compensation input for cancelling out the fault's effect on the system. Each filter is set up such that the residual is decoupled from unknown disturbances and fault with the influence vector designed in the filter. Simulation results for the simplified longitudinal flight control system with parameter uncertainties, process and sensor noises demonstrate the effectiveness of the present approach.

• 한국자동차공학회논문집
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• 제20권1호
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• pp.1-7
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• 2012

The brake-by-wire technology is a new automotive chassis system that allows standard braking operations by electronic components with lighter weights and faster response. The brake-by-wire units such as EMB (Electro-Mechanical Brake) are controlled by electronic sensors and actuators and, thus, the fault diagnosis is essential for implementation. In this study, a model-based fault diagnosis system is developed for the sensors based on the analytical redundancy method. The fault detection algorithm is verified in simulations for various faulty cases. A test bench is built including the EMB unit and the performance of the proposed fault diagnosis system is evaluated through the experiment.

• 한국인터넷방송통신학회논문지
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• 제19권1호
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• pp.29-35
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• 2019

4차 산업 혁명이 진행되며 많은 회사들의 스마트 팩토리에 대한 관심이 커지고 있으며 센서의 중요성 또한 대두되고 있다. 정보를 수집하기 위한 센서에서 고장이 발생하면 공장을 최적화하여 운영할 수 없기 때문에 이에 따른 손해가 발생할 수 있다. 이를 위해 센서의 상태를 진단하여 센서의 고장을 진단하는 일이 필요하다. 본 논문에서는 디지털 센서의 고장유형 중 Rising time과 Falling time 고장을 딥러닝 알고리즘 RNN의 LSTM을 통해 신호를 분석하여 고장을 진단하는 모델을 제안한다. 제안한 방식의 실험 결과를 정확도와 ROC 곡선 그래프의 AUC(Area under the curve)를 이용하여 Rule 기반 고장진단 알고리즘과 비교하였다. 실험 결과, 제안한 시스템은 Rule 기반 고장진단 알고리즘 보다 향상되고 안정된 성능을 보였다.