• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.569-575
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• 2022

This is a research thesis on the development of a monitoring and diagnosis device that prevents the risk of an accident through monitoring and diagnosis of a modular Main Circuit Breaker (MCB) using Vacuum Interrupter (VI) for Korean high-speed rolling stock. In this paper, a comprehensive MCB monitoring and diagnosis was performed by converging vacuum level diagnosis of interrupter, operating coil monitoring of MCB and environmental temperature/humidity monitoring of modular box. In addition, to develop an algorithm that is expected to have a similar data processing before the actual field test of the MCB monitoring and diagnosis device in 2023, the cluster analysis and factor analysis were performed using the WEKA data mining technique on the big data of Korean railroad transformer, which was previously researched by Tae Hee Evolution with KORAIL.

• KIEE International Transactions on Electrophysics and Applications
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• 제5C권3호
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• pp.131-137
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• 2005

This paper describes a new approach to the algorithm and fundamental characteristics of the device for monitoring the leakage currents flowing through zinc oxide (ZnO) surge arresters. In order to obtain a technique for a new on-line monitoring device that can be used in the deterioration diagnosis of ZnO surge arresters, the new algorithm and on-line leakage current detection device for extracting the resistive and capacitive currents using the phase shift addition method were proposed. The computer-based on-line monitoring device can sense accurately the power frequency leakage currents flowing through ZnO surge arresters. The on-line leakage current monitoring device of ZnO surge arresters proposed in this work has the high sensitivity compared to the third harmonic leakage current detection devices. As a consequence, it was found that the proposed leakage current monitoring device would be useful for forecasting the defects and degradation of ZnO surge arresters.

• 드라이브 ㆍ 컨트롤
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• 제18권1호
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• 한국정밀공학회지
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• 제18권10호
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• pp.33-44
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• 2001

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.455-460
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• 2007

A self-sensing circuit for piezo inkjet has been designed in order to monitor the operating condition during printing. In order to verify the circuit, both ink droplet images from strobe LED and vibration signals from the laser vibrometer were measured and compared with self-sensing signal. Experimental results show that self-sensing signal was effective in detecting the pressure wave change due to the bubble trapped in inkjet printhead.

• 한국전기전자재료학회논문지
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• 제30권9호
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• pp.596-601
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• 2017

The advancement and diversification of urban functions has caused an increasing need to improve the reliability of power supplies. The diversification of urban areas causes social disruptions by paralyzing urban functions during power outages. A large power outage occurs in the event of an accident, owing to the subduction of distribution lines. Therefore, in recent years, for the sake of the environment and safety, the safety diagnosis of electric power facilities has become an important issue. In this system, because thermal information changes rapidly during unattended monitoring owing to heat concentration phenomenon due to abnormal load or deterioration, studies have been conducted on the development of a device that can notify the manager at all times.

• Design & Manufacturing
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• 제16권4호
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• 한국인터넷방송통신학회논문지
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• 제18권6호
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• pp.187-193
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• 2018

국내에서의 전력에 대한 수요는 점진적으로 증가하고 있는 추세이다. 수요에 대응하기 위한 발전소는 기능이 다양해지고 효율이 점차 커져야 한다. 발전소 내의 전력 기기에서 사고가 발생하면 막대한 경제적 손실 및 장애를 초래하게 된다. 사고 발생의 원인 중의 하나로서 절연 성능이 저하 된 케이블이 있다. 케이블 사고를 미연에 방지하기 위하여 절연의 상태를 감시하고 확인하여야 한다. 케이블 사고는 연결 부위인 접속에서 발생하는 사고가 대부분을 차지한다. 본 연구와 관련하여 우리는 접속부 상태를 판별하기 위한 장비를 개발하였고, 한국서부발전(주)의 현장에 설치하여 운용 중이다. 본 논문에서는 현장에서 설치 운용 중인 장비에 대한 설명과 더불어 설치 운용된 결과를 통해 케이블의 수명을 예측할 수 있는 고전압 케이블 접속재의 안정적 사용을 위한 온라인 감시진단기법 중에서 사고가 가장 빈번하게 발생하는 접속부에 대해 안정적인 사용을 위한 진단의 정확성과 신뢰성을 향상시키기 위한 연구를 하였다. 이 논문에서 하드웨어 구성을 위주로 우리가 개발한 장비를 소개한다.

• 한국태양에너지학회 논문집
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• 제38권3호
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.