• Proceedings of the KSR Conference
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• 2011.10a
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• pp.737-747
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• 2011

5678SMRT has installed various sensor for operating conditions(field of electric, facilities, signal, communication equipment and track) and environment of Every Function Room for remotely detecting and monitoring. Installed sound sensor for analyzed after remotely heard the noise of every equipment at Every Function Room and temperature sensor for check the temperature condition of Every Function Room. Additional installed voltage sensor in signal equipment room for monitoring RF track-circuit's voltage condition. Installed displacement sensor at The Chungdam bridge's railway for measuring and monitoring track displacement caused by temperature change and Pan/Tilt camera at sub-station and drainage for remotely field monitoring. Installed sensor for each equipment's operating condition and failure at Every Function Room then periodic check of workforce turned to around-the-clock surveillance by sensor therefore improvement of operating equipment. SMRT is lots of prevent a failure by Immediately detect of precondition of equipment failure by analyzed the sensor data. If the occurrence of an failure, become detected Immediately so possibility correct diagnosis and order by remotely field check by installed camera and sound sensor at field.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.142-147
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• 2012

Machining tool conditions directly affect to quality of product and productivity of manufacturing. Many researches performed for tool condition monitoring in machining process to improve quality and productivity. Conventional methods use characteristics of signal for cutting force, motor current consumption, vibration of machine tools and machining sound. Recently, diameter of machining tool is become smaller for minimizing of mechanical parts. Tool condition monitoring using conventional methods are relatively difficult because micro machining using small diameter tool has low machining load and high cutting speed. These days, the direct monitoring for tool conditions using vision system is performed actively. But, vision system is affected by external conditions such as back ground of image and illumination. In this study, minimizing technology of external conditions using distribution analysis of image data are developed in micro machining using small diameter drill and tap. The image data is gathered from vision system. Several sets of experiment results are performed to verify the characteristics of the proposed machining technology.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.138-148
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• 1995

While Model-based Monitoring systems based on state observer theory have shown much promise in the laboratory, they have not been widely accepted by industry because, inpractice, these systems often have poor performance with respect to accuracy, band-width, reliability(false alarms), and robustness. In this paper, the linitations of the deterministic discrete-time state observer are investigated quantitatively from the machine monitoring viewpoint. The limitations in the transient and steady-state observer performance are quantified as estimation error bounds from which performance indices are selected. Each index represents the conditioning of the corresponding performance. By utilizing matrix norm theory, an unified main index is determined, that dominates all the indices. This index could from the basis for an observer design methodology that should improve the performance of model-based monitoring systems.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.101-102
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• 2022

The current facility condition monitoring has disadvantages such as a slow inspection cycle, a risk of human casualties, and the need for a lot of time and money as the size of the structure is larger, because human access is required with limited use. Drones can reduce the risk of human casualties due to their good accessibility, and can compensate for the shortcomings of the current method by enabling monitoring on a wide scale. The goal of this study is to provide the current domestic monitoring process through benchmarking according to the recent research case of the US Department of Transportation (DOT) to suggest a process suitable for the domestic situation and the direction of future improvement measures.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.245-252
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• 2020

Unexpected equipment defects can cause a huge economic losses in the society at large. Although condition monitoring can provide solutions, the signal processing algorithms must be developed to predict mechanical failures using data acquired from various sensors attached to the equipment. The signal processing algorithms used in a condition monitoring requires high computing efficiency and resolution. To improve condition monitoring on a wireless sensor network(WSN), data visualization can maximize the expressions of the data characteristics. Thus, this paper proposes the extraction of visual feature from temperature data over time using condition monitoring based on a WSN to identify environmental conditions of equipment in a large-scale infrastructure. Our results show that time-frequency analysis can visually track temperature changes over time and extract the characteristics of temperature data changes.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2110-2111
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• 2008

Continuous on-line temperature monitoring allows corrective measures to be taken to prevent upcoming failure. Continuous temperature monitoring and event recording provides information on the energized equipment's response to normal and emergency conditions. On-line temperature monitoring helps to coordinate equipment specifications and ratings, determine the real limits of the monitored equipment and optimize facility operations. Using wireless technique eliminates any need for special cables and wires with lower installation costs if compared to other types of online condition monitoring equipment. In addition, wireless temperature monitoring works well under difficult conditions in strategically important locations. Wireless technology for on-line condition monitoring of energized equipment is applicable both as standalone system and with an interface with power quality monitoring system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2185-2191
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• 2012

The monitoring of the existing inverter is being implemented using a local computer of web monitoring, but in this paper, the remote monitoring system of the power condition system was implemented using a Bluetooth communication at a convenient position for the user that can be monitored without the computer. The proposed system was designed to be able to monitor the wanted information by using the protocol of inverter. Also when the power condition system has failed, the fault history and the generated time of inverter were stored in the Bluetooth device. Finally the performance of the proposed system was evaluated through experiments, it showed the good performance and the possibility of commercialization.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.667-672
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• 2013

This paper presents the development of a condition monitoring system that monitors the operating conditions of a reduction unit, such as the bearing temperature, gearbox vibration, and gear oil deterioration, and notifies the operator of potential problems or abnormal conditions. A series of field tests on high-speed rail and conventional lines was performed to identify the characteristics of temperature rise and vibration levels on the reduction unit during operation. The monitoring system was designed based on the proper sensor selection, measurement method, and signal analysis to optimize the interface with the operating system of high-speed trains. Application of this monitoring system to high-speed trains will play an important role in their proper maintenance and safe operation.

• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.