• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.45-51
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• 2004

Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.4
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• pp.323-330
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• 2008

High cycle bending fatigue of socket welded small bore pipe was characterized, and also the fatigue crack initiation of small bore pipe was monitored in situ by the acoustic emission (AE) technique. The STS 316L stainless steel specimens were prepared by gas tungsten arc welding (GTAW) process having the artificial defect (i.e., lack of penetration) and defect free at the root. The fatigue failure was occurred at the loc for high stress and root for relatively low stress. The crack initiation cycles ($N_i$) was defined to the abrupt increase in AE counts during the fatigue test, and then the cracks were observed by the radiographic test and electron microscope before and after the fatigue crack initiation cycles. The socket welded pipe damaged by bending fatigue was studied regarding the welding defect, failure mode, and crack initiation cycles for the diagnosis and monitoring.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.665-666
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• 2004

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.331-332
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• 2006

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.220-226
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• 2003

This paper presents the result of an investigation on the application of the acoustic emission method to the monitoring of fatigue crack initiation, growth and track location in welded joints. Fatigue test was carried out for a typical fillet welded joint of ship structure. AE parameter such as ring down count was analyzed in time domain and crack locations were examined by source location and cluster option which is one of the functions of AE signal processor The usability of AE mettled was confirmed for the detection of the initiation and location of through crack.

• Composites Research
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• v.22 no.6
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• pp.45-51
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• 2009

The marginal integrity at the composite resin-tooth interface has been analyzed in real time through acoustic emission (AE) monitoring during the polymerization shrinkage of composite resin subjected to the light exposure. It was found that AE signals were generated by the polymerization shrinkage. Most AE hit events showed a blast type signal having the principal frequency band of 100-200kHz. Bad bonding states were indicated by many hit events in the initial curing period of 1 minute with high contraction rate. The quantity of hit events for the human molar dentin specimen was much less than that for the steel ring specimen but more than that for the PMMA ring specimen. The better the bonding state, the less the AE hit events. The AE characteristics were related with the tensile crack propagation occurring in the adhesive region between the composite resin and the ring substrate as well as the compressive behavior of the ring substrate, which could be used for a nondestructive characterization of the marginal disintegrative fracture of the dental restoration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.130-134
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• 1996

The characteristics of AE(Acoustic Emission) signal is related to cutting conditio, tool material, and tool geometry in metal cutting. The relation between AE signal and tool life was investigated experimentally. Experiment is carried out by gear shaping and SCM 420 workpiece. AE RMS voltage were increased according totool wear. It is suggested that maximum value of AE RMS voltage is an effective parameter to monitor tool life.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.186-189
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• 1997

To monitor abnormal state of rotary compressor, methods for acquisition and processing of Acoustic Emission(AE) signal are arranged and optimal AE parameter for monitoring is determined. The detecting method of abnormal compressor in real time is suggested and special-purpose minitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor in low cost.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.26-31
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• 1994

The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and process and as a methodology for detecting tool fracture on line. In this study, AE signals detected during End Milling were applied as the experimental test to sensing tool fracture on the CNC vertical milling machine. Because automatic monitoring of the cutting condition is one of the most important technologies in machining, the in-process detection of cutting tool life including fracture has been investigated by performing experimental test.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.662-668
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• 2005

This paper presents a realization of check valve condition monitoring system based on fault diagnosis algorithm and Fieldbus communication. We first acquired AE(acoustic emission) sensor data at the check valve test loop, extract fault features through the teamed neural network, and send the processed data to a remote site. The overall system has been implemented and experimented results are given to show its effectiveness.