• 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.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.632-637
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• 2017

Even though the potential for an accident in nuclear power plants is very low, multiple emergency plans are necessary because the impact of such an accident to the public is enormous. One of these emergency plans involves a robotic system for investigating accidents under conditions of high radiation and contaminated air. To develop a robot suitable for operation in a nuclear power plant, we focused on eliminating the three major obstacles that challenge robots in such conditions: the disconnection of radio communication, falling on uneven floors, and loss of localization. To solve the radio problem, a Wi-Fi extender was used in radio shadow areas. To reinforce the walking, we developed two- and four-leg convertible walking, a floor adaptive foot, a roly-poly defensive falling design, and automatic standing recovery after falling methods were developed. To allow the robot to determine its location in the containment building, a bar code landmark reading method was chosen. When a severe accident occurs, this robot will be useful for accident condition monitoring. We also anticipate the robot can serve as a workman aid in a high radiation area during normal operations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.360-363
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• 2009

Total monitoring device of leakage current in transformer can detects the flowing leakage current on the neutral line, and that is monitored at real-time by the transfer. If the over-flowing current is detected Total monitoring device of leakage current in transformer will warn the administrator to prevent electrical accident. In this thesis, we are going to explain about Total monitoring device of leakage current in transformer and the necessity of Total monitoring device of leakage current in transformer through real-accident cases and equipment condition in Korea.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.803-816
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• 2014

Defense-in-depth is a fundamental safety principle for the design and operation of nuclear power plants. Despite its general appeal, defense-in-depth is not without its drawbacks, which include its potential for concealing the occurrence of hazardous states in a system, and more generally rendering the latter more opaque for its operators and managers, thus resulting in safety blind spots. This in turn translates into a shrinking of the time window available for operators to identify an unfolding hazardous condition or situation and intervene to abate it. To prevent this drawback from materializing, we propose in this work a novel safety principle termed "observability-in-depth". We characterize it as the set of provisions technical, operational, and organizational designed to enable the monitoring and identification of emerging hazardous conditions and accident pathogens in real-time and over different time-scales. Observability-in-depth also requires the monitoring of conditions of all safety barriers that implement defense-in-depth; and in so doing it supports sensemaking of identified hazardous conditions, and the understanding of potential accident sequences that might follow (how they can propagate). Observability-in-depth is thus an information-centric principle, and its importance in accident prevention is in the value of the information it provides and actions or safety interventions it spurs. We examine several "event reports" from the U.S. Nuclear Regulatory Commission database, which illustrate specific instances of violation of the observability-in-depth safety principle and the consequences that followed (e.g., unmonitored releases and loss of containments). We also revisit the Three Mile Island accident in light of the proposed principle, and identify causes and consequences of the lack of observability-in-depth related to this accident sequence. We illustrate both the benefits of adopting the observability-in-depth safety principle and the adverse consequences when this principle is violated or not implemented. This work constitutes a first step in the development of the observability-in-depth safety principle, and we hope this effort invites other researchers and safety professionals to further explore and develop this principle and its implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4186-4192
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• 2012

Because wind turbines are larger and more off-shore construction due to economic and environmental factors, it is more difficult to access the wind turbine as well as the necessary parts and the maintenance costs are increasing. So, we need to minimize fault elements and to prevent a secondary accident at failure through monitoring to reduce maintenance costs and to increase reliability of operation. In this paper we have implemented ZigBee based wireless sensor nodes and network for wind turbine condition monitoring using temperature, humidity, voltage, current, wind direction, and wind speed sensors. ZigBee wireless sensor nodes signals are transmitted to a central monitoring system via routers. Also, the sensor signals are collected and processed using LabVIEW program to monitor the wind turbine conveniently. The administrators and users can monitor the condition of wind turbine at remote site in real time over TCP/IP.

• Journal of Platform Technology
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• v.6 no.4
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• pp.41-48
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• 2018

Accidents involving infants and toddlers are occurring frequently, and if elementary measures are not smooth, they can lead to even greater accidents. Rapid elementary measures require an appropriate monitoring system, and it is necessary to build a system that can accurately monitor the child's living condition and notify the emergency situation immediately. Recently, with the development of IoT technology, various types of sensors are being developed in a very small size, and development of wearable devices connected with smart phones is accelerating with the development of smart phones. By using these wearable devices, it is possible to reduce the risk of infant accidents and to respond promptly in case of an accident. In this paper, we propose a wearable system that can monitor the infant condition and present a service to prevent infant accidents.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• Journal of the Korean Society of Safety
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• v.28 no.5
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• pp.78-82
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• 2013

The purpose of this study is to review the labor force partitpation rate and work environment characteristics of female workers to provide basic information for establishment and implementation of effective policies related to accident prevention and workers health protection for female workers. It was analyzed employment status, work environment and injuries and illnesses based on economic activity census results, compensation of industrial injury and the second working condition survey. According to economic activity census results, female labor force participation rate has been steadily increased, whereas male labor force participation rate has been decreased since 1970. Industrial accident rate has been declined in male workers but that in female workers has been steadily increased even though male workers were higher accident rate than female workers. It was evaluated that female workers are vulnerable to industrial injuries and illnesses in the aspect of their working environment and employment status. Also, Substantial differences between female and male workers in occupational exposure patterns, occupational disease and occupational environments were observed. Therefore it was recommended that special care programs for female workers such as a specialized monitoring and management program should be introduced in the near future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.200-206
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• 2016

It is necessary to develop a real-time monitoring system for electric facilities, operating and managing system for the accident prevention of electrical demand facilities anytime, anywhere. In this paper, we propose the implementation of distributing board monitoring system based on Internet of Things(IoT). The proposed system is installed in existing distributing board that it can transmit status information of distributing board and control information through the cloud server and the wireless local area network. The distributing board monitoring system can monitor and control the condition of distributing board by system administrator. The results show that the margin of error was ${\pm}5%$ in performance evaluation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.5
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• pp.72-78
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• 2010

A CFRP (Carbon Fiber-Reinforced Plastic) strengthening method is being very widely used to increase the load-carrying capacity of host structures, especially for bridges. However, not only flexure and shear failures but debonding failure also might occur in CFRP strengthened concrete structures. The CFRP debonding failure would cause a collapse accident of the host structure. Therefore, real-time health monitoring about the CFRP bonding condition is strongly required. In this study, a feasibility of the impedance-based damage detection method using PZT sensors is investigated through a series of experimental study monitoring both concrete cracks and CFRP debonding defects.