• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.3
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• pp.407-413
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• 2010

This paper reports on the research which analyzes acoustic signals acquired in progressive compressing, hole blanking, and burr compacting process. An acoustic sensor was set on the bed of hydraulic press. Acoustic signal is generated from progressive stamping process. First the signal acquired from the unit process; compressing, blanking or compacting, is studied by Fourier Transform and Short Time Fourier Transform. The blanking process emitted ultrasonic signal with more than 20kHz, but the compressing and compacting processes emitted acoustic signals with lower than 10kHz. The combined signals periodically acquired right after the tool grinding were then analyzed. 70-80kHz signals appeared in time-frequency domain, but not in the frequency domain, the magnitude of which was related to the tool wear. Short Time Fourier Transform made up for the Fourier Transform in analyzing the emitted signal for stamping process in the ultrasonic domain.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1468-1473
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• 2013

This paper dealt with the propagation characteristics of acoustic signals produced by partial discharges and the positioning of PD origin in insulation oil to develop insulation diagnostic techniques of oil-immerged transformers. Electrode systems such as needle to plane, plane to plane, and particle electrodes were fabricated to simulate some defects of power transformers. In addition, the frequency spectrum and propagation characteristics of acoustic signals with partial discharge (PD) in insulation oil were analyzed. Although there were differences based on the type of defect, the frequency spectra of the acoustic signals measured by wide and narrow band acoustic emission (AE) sensors were distributed in the range of 50 kHz-400 kHz. Therefore, a narrowband AE sensor is suitable for the diagnosis of oil-immersed power transformers. We could find the position of the PD source with an error margin of 10% in the experiments by calculating the position of the PD occurrence using the time difference of arrival measured by five AE sensors.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.63-72
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• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.2
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• pp.147-152
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• 2012

This paper dealt with the frequency component analysis of acoustic signals produced by corona and series-arc discharges as a diagnostic technique for closed-switchboards. Corona and series-arc discharge were simulated by a needle-plane electrode and an arc generator specified in UL1699, respectively. Acoustic signal was detected by a wideband acoustic sensor with a frequency bandwidth of 4 Hz~100 kHz (-3 dB). We analyzed frequency spectrums of the acoustic signals detected in various discharge conditions. The results showed that acoustic signals mainly exist in ranges from 30 kHz to 60 kHz. From the experimental results, an acoustic detection system which consists of a constant current power supply (CCP), a low noise amplifier (LNA) and a band pass filter was designed and fabricated. The CCP separates the signal component from the DC source of acoustic sensor, and the LNA has a gain of 40 dB in ranges of 280 Hz~320 kHz. The high and the low cut-off frequency are 30 kHz and 60 kHz, respectively. We could detect corona and series-arc discharges without any interference by the acoustic detection system, and the best frequency is considered in ranges of 30 kHz~60 kHz.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.11a
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• pp.95-100
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• 1991

From a manufacturing standpoint it would be desirable to monitor the degradation of drawing die, that is essential for the maintenance of quality, the evaluation of product integrity and the reducing scrap. Acoustic emission is powerful method in monitoring fine wire drawing process, especially in detecting the die fracture at early stage. Experiments at so suggested that acoustic emission signals contained valuable information regarding the stage of a drawing process such as the surface appearance of products and the condition of lubrication. Using these informations makes AE monitoring techniques a possible tool in monitoring the drawing process operation. In order to approach this, this paper discusses the nature of acoustic emission signals produced in drawing process under various conditions. Experimental results are presented which illustrate the effects of wire and die material, lubricants, and drawing speed on the generation and the mean voltage level of acoustic emission signals. The results from these tests give controlling factors of acoustic emission generation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.4
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• pp.449-457
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• 2016

Cylindrical array sensor of a surface ship to detect an enemy is normally installed in the sonar dome. Reflected signals by some structures inside the sonar dome make unwanted signals. To minimize unwanted signals, acoustic baffles are used. Acoustic baffles are hard to install and replace, so the durability of acoustic baffles is an important design parameter. To verify the durability of acoustic baffle, accelerated aging tests according to temperature and pressure were performed. Acoustic baffle specimens were made and they are tested the visual and the performance (echo reduction and transmission loss) inspection before and after aging. After the inspection, the effect of accelerated aging of the acoustic baffles were discussed.

• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.25-34
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• 2003

The research on the development of the fault monitoring system for the thermal reduction reactor has been performed preliminarily in order to support the successful operation of the thermal reduction reactor. The final task of the development of the fault monitoring system is to assure the integrity of the thermal$_3$ reduction reactor by the acoustic emission (AE) method. The objectives of this paper are to identify and characterize the fault-induced signals for the discrimination of the various AE signals acquired during the reactor operation. The AE data acquisition and analysis system was constructed and applied to the fault monitoring of the small- scale reduction reactor, Through the series of experiments, the various signals such as background noise, operating signals, and fault-induced signals were measured and their characteristics were identified, which will be used in the signal discrimination for further application to full-scale thermal reduction reactor.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.9-20
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• 1999

To investigate the characteristics of internal waves (IWs) and their effects on acoustic wave propagation, a series of sea experiment were performed in the east coast of Donghae city, Korea in 1997 and 1998 where the water depth varies between 130 and 140 m. Thermistor strings were deployed to measure water temperatures simultaneously at 9 depths. CW source signals with the frequencies of 250,670 and 1000 Hz were received by an array of 15 hydrophones. Through the Wavelet transform analysis, the IWs are characterized as having typical periods of 2-17 min and duration of 1-2 hours. The IWs exist in a group of periods rather than in one period. Underwater acoustic signals also show obvious energy peaks in the periods of less than 12 min. Consistency in the periods of the two physical processes implies that acoustic waves react to the IWs through some mechanisms like mode interference and travel time fluctuation. Based on the thermistor string data, mode arriving structures are analyzed. As thermocline depth varies with time, it may cause travel time difference as much as 4-10 ms between mode 1 and 2 over 10 km range. This travel time difference causes interference among modes and thus fluctuation from range-independent stratified ocean structure. In real situations, however, there exist additional spatial variation of IWs. Model simulations with all modes and simple IWs show clear responses of acoustic signals to the IWs, i.e., fluctuations of amplitude and phase.