• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.437-441
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• 1999

In this paper, a fault diagnosis method using unknown-input proportional integral (PI) observers including the magnitude of actuator failures is proposed. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between the actual output and the estimated output using an unknown-input PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple unknown-input PI observers perfectly.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.257-260
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• 1997

Bluetooth is the most promising network paradigm which ca open the new area in the information technology. Especially, bluetooth can link all the electrical products and PCs(Personal Computer) to cellular phone or PDA. In this paper, the data from ECU which are gathered by scanner are communicated between tow PCs using the bluethooth modules. The acquired data are ECU's self diagnosis signal and sensor output signal. Self diagnosis signals are very important to check the ECU's state and sensor output signals. Using these data, the possibility of wireless communication with ECU is developed and verified. Protocol stack of bluetooth is L2CAP through HCI and wireless communication software of ECU's signal is developed using VC++ in Windows 98 environment.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.7
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• pp.449-453
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• 2016

The amount of electrical energy has been increased with the rapid development of the industrial society. Accordingly, operating voltage of the power equipment and facility capacity are continuously increasing. Development trends of recent high-voltage electrical equipment are ultra high-voltage, large-capacity and compact. Early diagnosis of a failure of the power plant has been emerging as an important task as to supply high quality power to users. In this study, we have tried to develope an algorithm for distinguishing an arc fault signal generated in the power plant by using UV sensor.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1002-1009
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• 2024

An economizer control is used for cooling a building by modulating outdoor air(OA) intake rate according to measured OA conditions. An OA enthalpy sensor can be faulty during the operating after installation. The sensor mainly is fault in the form of offset. It leads value differences between measured enthalpy and actual enthalpy. The enthalpy differences occurred by the faulty sensor may result in more OA intake or less OA intake than designed OA intake value. The unwanted amount of OA intake negatively affects cooling system performance, especially cooling energy consumption. Therefore, this study analyzed cooling system performance resulted from occurring the faulty sensor in economizer enthalpy control. To conduct the analysis, this study utilized the Fault model in EnergyPlus, a building energy simulation tool. As a result of the analysis, the faulty sensor with positive offset intaked less OA amount than the available OA amount. It lead more cooling energy consumed by cooling equipment such as chiller and circulation pump. On the other hand, the faulty sensor with negative offset intaked more unnecessary OA amount than the required OA amount. It also lead more cooling energy consumption in the cooling equipment. Based on the resultant analysis, this study suggests continuous maintenance and diagnosis for an enthalpy sensor used in the economizer system. It may allow proper operation control for the economizer system, and thus the maximum cooling energy saving can be achieved.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.807-813
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• 2006

The objective of this study is to develop a rule-based fault detection algorithm and an experimental verification using an artificial air handling unit. To develop an analytical algorithm which precisely detects a tendency of faulty component, energy equations at each control volume of AHU were applied. An experimental verification was conducted on the HVAC simulator. The rule based FDD algorithm isolated a faulted sensor from HVAC components in summer and winter conditions.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.405-410
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• 2016

Elevator industry is a field that is mechanical, electrical and electronic technology and constantly requires inspection and maintenance considering various applications and various types. Recently, various elevator control and monitoring technologies with IT are developing for elevators on land. But technologies with IT have been hardly done in marine elevator that is consistently assured safety and reliability of life cycle for its parts in poor environment. In this paper, we implemented embedded main controller, floor controller and car controller that meet the requirements and use NMEA network protocol by analyzing home and abroad integrated elevator operation and management systems. Especially, we secured reliability of maintenance by real-time fault diagnosis and control that was implemented with limit switch, gyro sensor, temperature/humidity/barometric pressure sensor and fire detection sensor thinking over the environmental conditions of terrestrial and marine elevator.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.