• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.21 no.1
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• pp.85-89
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• 2008

An objective of this paper is to develop a partial discharge (PD) measurement device for monitoring gas insulated switches installed in power distribution system. A capacitive voltage probe was studied and designed to detect PD pulse without an electrical connection. The PD measurement device consists of the capacitive voltage probe attached outside of a bushing, a coupling network which attenuates AC voltage by 270 dB, and a low noise amplifier with the gain of 40 dB in ranges of 500 kHz${\sim}$20 MHz. The sensitivity of the prototype device calculated by a calibrator was 1.98 m V /pc. An application experiment was carried out in a 25.8 kV gas insulated switch and the peak pulse of 76.7 pC was detected. From the experimental results, it is expected that the PD measurement device can be applied to online monitoring system of gas insulated switches.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Smart Structures and Systems
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• v.16 no.2
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• pp.281-294
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• 2015

Bolted joint connection is the most commonly used connection element in structures and devices. The loosening due to external dynamic loads cannot be observed and measured easily and may cause catastrophic loss especially in an extreme requirement and/or environment. In this paper, an innovative Real-time Cross-Correlation Method (RCCM) for monitoring of the bolted joint loosening was proposed. We apply time reversal process on stress wave propagation to obtain correlation signal. The correlation signal's peak amplitude represents the cross-correlation between the loosening state and the baseline working state; therefore, it can detect the state of loosening. Since the bolt states are uncorrelated with noise, the peak amplitude will not be affected by noise and disturbance while it increases SNR level and increases the measured signals' reliability. The correlation process is carried out online through physical wave propagation without any other post offline complicated analyses and calculations. We implemented the proposed RCCM on a single bolt/nut joint experimental device to quantitatively detect the loosening states successfully. After that we implemented the proposed method on a real large structure (reaction wall) with multiple bolted joint connections. Loosening indexes were built for both experiments to indicate the loosening states. Finally, we demonstrated the proposed method's great anti-noise and/or disturbance ability. In the instrumentation, we simply mounted Lead Zirconium Titanate (PZT) patches on the device/structure surface without any modifications of the bolted connection. The low-cost PZTs used as actuators and sensors for active sensing are easily extended to a sensing network for large scale bolted joint network monitoring.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.1-6
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• 2008

The ubiquitous sensor network technique is widely applied to variety of information fields such as home automations, logistics, traffic controls, public administrations, health and environment monitoring and etc. It is particularly useful in the areas where energy consumption is minimal and where continuous monitoring of the surrounding environments, which generates streams of data, are required. In this study, we have designed and implemented a living environment automatic control system which collects the streams of temperature, humidity, light and noise data of a simulated house setting in real-time fashion, then controls the home environment based on the collected data according to the users favorites. In order to differentiate the proposed system from the currently existing similar system, we have demonstrated not only the feasibility of collecting data using sensor network in the controlled environment but also the ability to control the various household equipments through wireless communications.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.667-675
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• 2012

A damage estimation method for support structure of offshore wind turbine using modal parameters is presented for effective structural health monitoring. Natural frequencies and mode shapes for a support structure with monopile of an offshore wind turbine were calculated considering soil condition and added mass. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. Natural frequencies and mode shapes for 10 prospective damage cases were input to the trained neural network for damage estimation. The identified damage locations and severities agreed reasonably well with the accurate damages. Multi-damage cases could also be successfully estimated. Enhancement of estimation result using another parameters as input to neural network will be carried out by further study. Proposed method could be applied to other type of support structure of offshore wind turbine for structural health monitoring.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1051-1057
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• 2009

USN(Ubiquitous Sensor Network) is used to provide many services such as bridge monitoring, cultural properties monitoring, river monitoring, protection of an old and feeble person, management and control of a city and circumstance monitoring, etc. A RF transceiver is needed for implementing USN. In this paper the implementation and the design of a RF transceiver for sensor nodes operating in 2.4GHz frequency band are presented. The design procedure of AGC, a receiver and a transmitter is described. And the performance of the implemented RF transceiver is also tested. The test results of receiver sensitivity, receiver dynamic range, frequency stability, phase noise, output power of transmitter, flatness and spectrum mask are presented.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.10
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• pp.10-20
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• 1994

This paper presents a CATV SMS (Status Monitoring System) using HDLC (High Level Data Link Contorl) protocol and the system implementation.The system specifications were derived from the analysis of technical status and requirement of the domestic CATV industry. For the interoperability with a global network management system in the future, HDLC protocol was adopted in the system. The system performance was improved by using the communication controller chip and the large data buffer. For reducting the communication problems induced by accumulated noise in up-stream data channels, the system was designed that the different communication channel can be assigned to each proper mass of terminal. The operating software was designed with menu driven user interface and have various functions for the convenience of users. The test result of the implemented system at the experimental network showed good performance and suitability for a coaxial CATV Status Monitoring System.