• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.324-328
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• 2007

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. Acoustic Emission (AE) Analysis is an extremely powerful technology that can be used within a wide range of applications of non destructive testing. Therefor, this paper investigates the detection methods using AE for rolling element bearings about low-speed. Two transducers, the accelerometer and acoustic emission sensor, are used to acquire data and the results are compared for the capacity of early fault detection.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.437-439
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• 2006

Actually, The present traffic accident detection system is subsisting limitation of accurate distinction under the crowded condition at intersection because the system defend upon mainly the image information at intersection and digital image processing techniques nearly all. To complement this insufficiency, this article aims to estimate the level of present technology and a realistic possibility by analyzing the acoustic characteristic of crash sound that we have to investigate for improvement of traffic accident detection rate at intersection. The skid sound of traffic accident is showed the special pattern at 1[kHz])${\sim}$3[kHz] bandwidth when vehicles are almost never operated in and around intersection. Also, the frequency bandwidth of vehicle crash sound is showed sound pressure difference oyer 30[dB] higher than when there is no occurrence of traffic accident below 500[Hz].

• Proceedings of the KSR Conference
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• 2000.11a
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• pp.733-739
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• 2000

This paper is described the development of on-line partial discharge detector for high voltage apparatus. This detector consists of acoustic and high frequency current sensors, amplifier part, A/D converter part, data communication part and computer. The contents of paper are characteristics of units and digital signal processing for reducing noise in partial discharge detection. We seek methods to do good digital signal processing for detection of partial discharge. We apply digital filtering methods to the elect Tic signal and a cross con-elation to the acoustic signal. This paper shows the characteristics of these filtering method and cross con-elation in partial discharge detection.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.71-77
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• 1999

Monitoring of Cutting tool wear is a critical issue in automated machining system and has been extensively studied for many years. An austempered ductile iron(ADI) exhibits the excellent mechanical properties and the wear resistance. ADI has generally the poor machinability due to the characteristic. This paper presents the in-process detection of flank wear of cutting tools using the acoustic emission sensor and the digital oscilloscope. The amplitude level of AE signal(AErms) is mainly affected by cutting speed and it is proportional to cutting speed. There have been the relationship of direct proportion between the amplitude level of AE signals and the flank wear width of cutting tool. The flank wear with corresponding to the tool life is successfully detected with the monitor-ing system used in this study.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.3
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• pp.130-143
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• 1995

This paper presents a new methodology for on-line tool breadage detection by sensor fusion of an acoustic emission (AE) sensor and a built-in force sensor. A built-in piezoelectric force sensor, instead of a tool dynamometer, was used to measure the cutting force without altering the machine tool dynamics. The sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A sighificant drop of cutting force was utilized to detect tool breakage. The algorithm was implemented on a DSP board for in-process tool breakage detection. Experiental works showed an excellent monitoring capability of the proposed tool breakage detection system.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.3
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• pp.92-100
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• 1995

This experimental study is mainly investigated on the mean cutting forces and AE(acoustic emission) parameters in order to detect and estimate the tool failure in the pachinig of SUS304 by face milling Mean cutting forces and AE parameters can detect the tool failure in face milling. Effective detection parameters are AE RMS, AE energy, AE count, AE duration, and z-direction mean cutting force. From the analysis of cutting tool failure detection, the tool failure of face milling is caused by sudden increasing of the cutting force.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1163-1169
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• 2014

As a preliminary study for detection techniques of $CO_2$ gas bubble plumes, we have conducted a comparative experiment on artificially generated $CO_2$ gas bubbles plume by using multibeam echosounder (MBES), single beam echosounder (SBES), and sub-bottom profiler (SBP). The rising speed of artificial gas bubbles is higher than references because of compulsory release of compressed gas in the tank. Compared to single beam acoustic equipments, the MBES detects wide swath coverage. It provides exact determination of the source position and 3D information on the gas bubble plumes in the water column. Therefore, it is shown that MBES can distinctly detect gas bubble plumes compared to single beam acoustic equipments. We can establish more effective complementary detection technique by simultaneous operation of MBES and SBES. Consequently, it contributes to improve qualitative and quantitative detection techniques by understanding the acoustic characteristics of the specific gas bubbles.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.881-890
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• 2022

As individual and group users actively use drones, the risks (Intrusion, Information leakage, and Sircraft crashes and so on) in no-fly zones are also increasing. Therefore, it is necessary to build a system that can detect drones intruding into the no-fly zone. General acoustic drone detection researches do not derive location-independent performance by directly learning drone sound including environmental noise in a deep learning model to overcome environmental noise. In this paper, we propose a drone detection system that collects sounds including environmental noise, and detects drones by removing noise from target sound. After removing environmental noise from the collected sound, the proposed system predicts the drone sound using Mel spectrogram and CNN deep learning. As a result, It is confirmed that the drone detection performance, which was weak due to unstudied environmental noises, can be improved by more than 7%.