• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 추계학술대회 논문집
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• pp.59-64
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• 1997

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; AEavg, AErms, AEmode, as the base working to monitor the tool wear with in-process. For this purpose, cutting tests were conducted on a CNC lathe with comprehensive cutting conditions.. It is known that AEavg and AErms are proportionaly increased as the increasing of cutting velocity and depth of cut respectively. It is also known that AEmode among three kinds of AE signals may be applied for in-process monitoring to make the self diagnosis system because of its stability to the variation of cutting condition.

• 한국생산제조학회지
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• 제7권6호
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• pp.64-71
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• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$

• 한국철도학회:학술대회논문집
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• 한국철도학회 2010년도 춘계학술대회 논문집
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• pp.1861-1867
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• 2010

This paper described the positioning algorithm of partial discharge in insulation oil by acoustic method for the application of an on-line diagnosis in oil-immersed transformers. In the experiment, five AE sensors having the resonant frequency of 150 kHz were used, and a signal conditioner was fabricated. A needle-plane electrode system which is composed of a needle with a curvature radius of $10{\mu}m$ and a plane electrode with a diameter of 60 mm was installed to simulate partial discharges in insulation oil. From the time difference of arrival (TOA) of acoustic signal, we calculated the location of partial discharge in insulation oil. In the experiment, an algorithm of positioning of PD occurrence by the time difference of arrival was proposed. From the experimental results, the positioning error of PD calculated by three AE sensors was within 4%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 C
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• pp.1781-1783
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• 1996

Recently, the necessity of establishing the way to diagnose the aging of insulation materials and to predict of insulation breakdown become important. The purpose of our work are to investigate the treeing phenomena with a artificial needle shape void by the charge of partial discharge and acoustic emission pulse in each phase angle area at the same time. We have analyzed the ${\Phi}-Q-n$ pattern and the insulation diagnosis of the samples using statistic operators such as charge magnitude and A.E pulse factor, skweness, kurtosis, G,C. Therefore, the relation between the charge of partial discharge and A.E pulse will be helpful and efficient to predict the breakdown just before the breakdown occure.

• Journal of Mechanical Science and Technology
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• 제14권7호
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• pp.773-781
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• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• 대한기계학회논문집A
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• 제32권11호
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• pp.905-910
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• 2008

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, a acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect her fault all at once gearbox using AE signal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 C
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• pp.1879-1881
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• 2003

In this paper, acoustic signals in $SF_6$ gas were analyzed using wavelet transform. For this, the PD sources in the $SF_6$ gas were divided into corona discharge surface discharge void discharge and crossing particle and acoustic signals were used to detect the PD sources. The measured signals were time-frequency distribution by wavelet transform and the features were extracted from the PD sources. As a result the characteristics of the PD sources were different. And this results is going to be used for basis diagnosis of $SF_6$ gas insulated apparatus.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.

• 대한후두음성언어의학회지
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• 제33권3호
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• pp.183-187
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• 2022

Dysphonia is well known as one of the otolaryngological symptoms of coronavirus disease 2019 (COVID-19) infection. The vocal changes of the COVID-19 condition have been reported in terms of parameters of multi-dimensional voice assessment, including acoustic analysis, auditory-perceptual evaluation, and psychometric assessment. However, there has not been a daily followup study in patients with acute COVID-19 infection. In this study, a 41-year-old male performed daily voice recordings of vowel phonation and passage-reading tasks during the self-quarantine period of one week. Compared to the normal voice status of the prepandemic period, voice abnormalities peaked on day two after the diagnosis of COVID-19 infection and recovered after one week.