• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.334-339
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• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.37-44
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• 2005

This paper presents fabrication techniques of the optical fiber position sensor (or spindle state monitoring. These include selection of components such as optical fibers, a laser-diode, a photo-diode, and op-amp IC of the signal process circuit. We also describe electric runout problem. The fabricated sensor has a linearity of $1.7\%$ FSO in the air gap range $0.1\~0.6mm$, a resolution of $0.37{\mu}m$ and a bandwidth of 6.3kHz. Finally, we have successfully operated a magnetic bearing spindle system using the sensors.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1099-1101
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• 2003

The diagnosis of power converter using auxiliary circuit connected to output terminal is proposed and verified by experimental results in this paper. On contrary to conventional diagnosis method, the proposed monitoring technique is possible to measure the state of power converter and unbalanced output caused by a misfiring of switch and unusual control signal. Conventional method gives a monitoring approaches for only a unusual operation of power converter. In this paper, three signals with auxiliary circuit are obtained and analyzed to see the state of electrolytic capacitor, power semiconductor switches and output voltage balance. 2kVA inverter is made and fisted to verify the proposed circuit.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.9
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• pp.101-108
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• 2007

State estimators are used to monitor the operating states of power systems in modern EMS. It iteratively calculates the voltage profile of the currently operating power system with voltage, current, and power measurements gathered from the entire system. All the measurements are usually assumed to be obtained simultaneously. It is practically impossible, however, to maintain the synchronism of the measurement data. Recently, phasor measurements synchronized via satellite are used for the operation of these power systems. This paper describes the modified state estimator used to support the processing of synchronized phasor measurements. Synchronized phasor measurements are found to provide synchronism of measurement data and improve the accuracy/redundancy of the measurement data for state estimation. The details of the developed state estimation program and some numerical results of operation are presented.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.49-68
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• 2008

The purpose of this paper is to extensively review the condition monitoring (CM) techniques using empirical models in an effort to reduce or eliminate unexpected downtimes in general industry, and to illustrate the feasibility of applying them to the nuclear industry. CM provides on-time warnings of system states to enable the optimal scheduling of maintenance and, ultimately, plant uptime is maximized. Currently, most maintenance processes tend to be either reactive, or part of scheduled, or preventive maintenance. Such maintenance is being increasingly reported as a poor practice for two reasons: first, the component does not necessarily require maintenance, thus the maintenance cost is wasted, and secondly, failure catalysts are introduced into properly working components, which is worse. This paper first summarizes the technical aspects of CM including state estimation and state monitoring. The mathematical background of CM is mature enough even for commercial use in the nuclear industry. Considering the current computational capabilities of CM, its application is not limited by technical difficulties, but by a lack of desire on the part of industry to implement it. For practical applications in the nuclear industry, it may be more important to clarify and quantify the negative impact of unexpected outcomes or failures in CM than it is to investigate its advantages. In other words, while issues regarding accuracy have been targeted to date, the concerns regarding robustness should now be concentrated on. Standardizing the anticipated failures and the possibly harsh operating conditions, and then evaluating the impact of the proposed CM under those conditions may be necessary. In order to make the CM techniques practical for the nuclear industry in the future, it is recommended that a prototype CM system be applied to a secondary system in which most of the components are non-safety grade. Recently, many activities to enhance the safety and efficiency of the secondary system have been encouraged. With the application of CM to nuclear power plants, it is expected to increase profit while addressing safety and economic issues.

• Computers and Concrete
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• v.20 no.5
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• pp.595-603
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• 2017

Piezoceramic transducers have been widely used in the health monitoring of civil structures. However, in most cases, they are used as sensors either to measure strain or receive stress waves. This paper proposes a method of using piezoelectric transducers as strain gauges and acoustic emission (AE) sensors simultaneously. The signals received by piezoceramic transducers are decomposed into different frequency components for various analysis purposes. The low-frequency signals are used to measure strain, whereas the high-frequency signals are used as acoustic emission signal associated with local damage. The b-value theory is used to process the AE signal in piezoceramic transducers. The proposed method was applied in the bending failure experiments of two reinforced concrete beams to verify its feasibility. The results showed that the extracted low-frequency signals from the piezoceramic transducers had good agreement with that from the strain gauge, and the processed high-frequency signal from piezoceramic transducers as AE could indicate the local damage to concrete. The experimental results verified the feasibly of structural health monitoring using piezoceramic transducers as strain gauges and AE sensors simultaneously, which can advance their application in civil engineering.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.92-95
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• 2019

Automation control and the data for control of industrial equipment for the diagnosis and prediction is a key to success in the 4th industrial revolution. It increases process efficiency and productivity through data collection, realtime monitoring, and the data analysis. However, university and research environment are still suffering from logging the data in manual way, and we occasionally loss the equipment data logging due to the lack of automatic data logging system. State variable presents the current condition of the equipment operation which is closely related to process result, and it is valuable to monitor and analyze the data for the equipment health monitoring. In this paper, we demonstrate the collection of equipment state variable data via programmable logic controller (PLC) and the visualization of the collected data over the Web access supervisory control and data acquisition (SCADA). Test vehicle for the implementation of the suggested SCADA system is a relay switched physical vapor deposition system in the university environment.