• Journal of Advanced Marine Engineering and Technology
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• v.37 no.5
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• pp.520-526
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• 2013

As the increasing of capacity and technology of power facilities, rotating machines are getting higher at capacity and voltage scale. Thus the monitoring and diagnosis of generators for fault detection has attracted intensive interest. In this paper, we developed fault diagnosis system for monitoring the fault operations in bad power systems. In order to verify the performance of this fault diagnosis system, we made the small scaled testing system which has the same winding structure of the real synchronous generator. The magnetic flux patterns in air-gap of a small-scale generator under various fault states as well as a normal state are tested by hall sensors and the fault detection system.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.763-765
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• 1999

Due to the improvement of PC Performance and Windows-based OS, PC-based control system is gradually taking over industrial control in the industry site. In this paper, we suggest the method to configure PC-based fault diagnosis system of I/O cards and the method to generate test signals to find out the short conditions of adjacent channels of cables which connect I/O cards with terminal blocks.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.293-297
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• 1992

For the computerized monitoring and diagnosis of the undesirable chip chatter which are major obstacles to FMS, a pattern recognition system based on multi-layer perception neural network is developed and the performance of the system is experimentally evaluated. Experimental results show that recognition of the two class state of normal or abnormal cutting gives satisfactory results with success rate of 81`91%. Therefore, the proposed system has possibility for use in monitoring and diagnosis of automatic manufacturing system

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.183-192
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• 2022

Function and performance degradation in tunnel structure can cause road's function loss, proactive maintenance is needed to prevent the initial damage from progressing to intensified damage. Inspection and diagnosis are implemented in accordance with regulations, but it does not fully reflect maintenance processes such as inspection and diagnosis, planning rehabilitation and carrying out. It was carried out for 5,540 cases inspection and diagnosis result in 1,728 tunnels was analyzed to suggest rational maintenance plan in this study.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.125.4-125
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• 2001

The problem of recongurable ight control is investigated, focusing on model reference adaptive control(MRAC) through imprecise fault diagnosis. The method integrates the fault detection and isolation(FDI) scheme with the model reference adaptive control, and can be implemented on-line and in real-time. The algorithm can cope with the fast varying parameters. The Simulation results demonstrate the ability of reconguration to maintain the stability and acceptable performance after a failure.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.87-87
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• 2000

In this paper, an integrated design of fault detection, diagnosis and reconfigurable control tot multi-input and multi-output system is proposed. It is based on the interacting multiple model estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural and/or parametric changes. This research focuses on the method to recover the performance of a system with failed actuators by switching plant models and controllers appropriately. The proposed scheme is applied to a fault tolerant control design for flight control system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.493-502
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• 2019

Recently, the installation of renewable energy including PV systems in distribution systems has increased energetically to cope with climate change and energy shortages according to the government's policy of renewable energy 3020. On the other hand, the electrical performance and lifespan of PV modules installed outdoors can be decreased considerably due to a range of deterioration phenomena depending on the ambient environmental factors. To overcome these problems, replacement of degraded PV modules with new ones is increased before the lifespan guaranteed by the makers of PV modules. Therefore, this paper proposes a disposal diagnosis algorithm to evaluate the time interval of the optimal replacement for PV modules according to performance degradation rate of PV modules. In addition, this study modeled an economic evaluation, which is composed of the cost and benefit of PV systems. From the simulation results based on the proposed modeling and algorithm to consider the performance degradation rate specifically, the replacement approach was found to have the best B/C ratio in 10th year with a 3[%] degradation rate and the disposal diagnosis algorithm of PV modules are useful and practical tools for economic evaluations of the replacement of PV modules.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.10
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• pp.806-813
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• 1991

In this paper, we suggested the diagnosis of the optical FSK transmitter output characteristics using the Micheloson interferometer. The transmitter is designed by the direct frequency modulation effect of a semoconductor laser diode. The optical FSK transmitter consists of a temperature stabilized semiconductor laser diode and an optimally designed equalizer. It is modulated by 100Mbit/s PRBS Generator to diagonosis of Optical FSK transmitter output characteristics. The Michelson Interferometer is used for the optical frequency discriminator which transforms the output frequency of the transmitter to the amplitude of light. The measurement of the performance of the transmitter to the amplitude of light. The measurement of the performance of the transmitter computer the transmitted data and their eye pattern with the simulation results, the received data and their eye pattern. As the result of experiment, the data transfer rate of the transmitters is 100Mbit/s.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.