• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.345-350
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• 2014

DC electrolytic capacitor is widely used in the power converter including PWM inverter, switching power supply and PFC Boost converter system because of its large capacitance, small size and low cost. In this paper, basic characteristics of DC electrolytic capacitor vs. frequency is presented and the real-time estimation scheme of ESR and capacitance based on the bandpass filtering is adopted to the single phase boost converter of uninterruptible power supply to diagnose its split dc-link capacitors. The feasibility of this real-time failure prediction monitoring system is verified by the computer simulation of the 5[kW] singe phase PFC half-bridge boost converter.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.335-346
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• 2022

Buckling failure is a typical slope instability mode that should be paid more attention to. It is difficult to provide systematic guidance for the monitoring and management of such slopes due to unclear mechanism. Here we examine buckling failure as the potential instability mode for a slope above a railway tunnel in southwest China. A comprehensive model test system was developed that can be used to conduct buckling failure experiments. The displacement, stress, and strain of the slope were monitored to document the evolution of buckling failure during the experiment. Monitoring data reveal the deformation and stress characteristics of the slope with different slipping mass thicknesses and under different top loads. The test results show that the slipping mass is the main subject of the top load and is the key object of monitoring. Displacement and stress precede buckling failure, so maybe useful predictors of impending failure. However, the response of the stress variation is earlier than displacement variation during the failure process. It is also necessary to monitor the bedrock near the slip face because its stress evolution plays an important role in the early prediction of instability. The position near the slope foot is most prone to buckling failure, so it should be closely monitored.

• Journal of Platform Technology
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• v.6 no.2
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• pp.19-25
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• 2018

Existing Photovoltaic(PV) monitoring system monitors the current, past power generation, all values of environmental sensors. It is necessary to predict solar power generation for efficient operation and maintenance on the power plant. We propose a method for estimating the generation of PV data based PV monitoring system with data accumulation. Through this, we intend to find the failure prediction of the photovoltaic power plant in proportion to the predicted power generation. As a result, the administrator can predict the failure of the system it will be prepared in advance.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.101-108
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• 2017

Recently, the occurrence of landslides has been increasing over the years due to the extreme weather event. Developments of landslides monitoring technology that reduce damage caused by landslide are urgently needed. Therefore, in this study, a strain ratio sensor was developed to predict the ground behavior during the slope failure, and the change in surface ground displacement was observed as slope failed on the field model experiment. As a result, in the slope failure, the ground displacement process increases the risk of collapse as the inverse displacement approaches zero. It is closely related to the prediction of precursor. In all cases, increase in displacement and reverse speed of inverse displacement with time was observed during the slope failure, and it is very important event for monitoring collapse phenomenon of risky slopes. In the future, it can be used as disaster prevention technology to contribute in reduction of landslide damage and activation of measurement industry.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.

• Smart Structures and Systems
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• v.29 no.6
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• Plant Journal
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• v.15 no.2
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• pp.56-61
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• 2019

In this study, the failure analysis of the internal tube occurred in the high pressure feedwater heater for power generation boiler of 500 MW supercritical pressure coal fired power plant was investigated. I suggested a prediction model that can diagnose internal tube failure by changing the position of level control valve on the shell side and the suction flow rate of the boiler feedwater pump. The suggested prediction model is demonstrated through additional cases of feedwater system unbalance. The simultaneous comparison of the shell side level control valve position and the suction flow rate of the boiler feedwater pump compared to the normal operating state value, even in the case of the high pressure feedwater heater for the power boiler, It can be a powerful prediction diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.174-179
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• 2012

The social and economic level of damages might be highly increased in the case of wide-area black-outages, because of heavy dependence of electricity. Therefore, the development of a wide-area protection relay intelligence techniques is required to prevent massive power outages and minimize the impact strength at failure. The frequency monitoring and prediction for wide-area protection relaying intelligence has been considered as an important technology. In this paper, a network-based frequency monitoring system developed for wide-area protection relay intelligence is presented. In addition, conventional techniques for frequency estimation are compared, and a method for advanced frequency estimation and measurement to improve the precision is proposed. Finally, an integrated monitoring system called K-FNET(Korea-Frequency Monitoring Network) is implemented based on the GPS and various energy monitoring cases are studied.