• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.04a
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• pp.139-142
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• 2004

As fiber reinforced composite materials are widely used in aircraft, space structures and robot arms, the study on the non-destructive testing methods of the composite materials has become an important research area for improving their reliability and safety. In this paper, the AE signal analyzer with the resonance circuit to extract the specified frequency of the acoustic emission signal were designed and fabricated. The noise levels of the fabricated AE signal analyzer by the disturbance such as impact or mechanical vibration had a very small value comparable to those of the conventional AE signal analyzer. Also, the crack detection capabilities of the fabricated AE signal analyzer under the static and dynamic tensile test were evaluated and compared with the conventional AE signal analyzer.

• Journal of Power System Engineering
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• v.16 no.1
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• pp.72-77
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• 2012

Acoustic emission (AE) is known to be sensitive to fracture process and so it was expected that AE data may propose as a means of monitoring the fracture information. The aim of this study is to analyze the characteristic of AE signal emitted from glass during Vickers indentation. To observe AE characteristics by surface effect, both glass and coating glass were studied. During Vickers indentation loading, AE signal resulted from penny-like crack is detected. During Vickers indentation unloading, AE signal resulted from both radial/median crack and lateral crack is detected. In case of indentation on glass, the emission energy(${\epsilon}$) is found to be approximately proportional to the fourth power of the crack length. In case of indentation on coating glass, the emission energy(${\epsilon}$) is approximately proportional to the crack length.

• Tribology and Lubricants
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• v.17 no.1
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• pp.40-55
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• 2001

Acoustic emission(AE) signal has been widely utilized to monitor the interaction at the head/disk interface. In this work the relationship between the AE signal and the state of contact between the slider and the disk is presented. Results of the FFT(Fast Fourier Transform) analyses of the AE signal could be used to better understand the interfacial interaction. Also, it was found that wear particles affect the AE signal. Therefore, the signal can be used to monitor the wear particle presence at the interface.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.11a
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• pp.310-317
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• 1999

Acoustic emission(AE) signal has been widely utilized to monitor the interaction at the head/disk interface. In this work the relationship between the AE signal and the state of contact between the slider and the disk is presented. Results of the FFT analyses of the AE signal could be used to better understand the interfacial interaction. Also, it was found that wear particles affect the AE signal. Therefore, the signal can be used to monitor the wear particle presence at the interface.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.1
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• pp.74-83
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• 1991

A study on the Correlation of Orthogonal Cutting Parameter (Cutting speed, Depth of cut, Feed Rate) and Acoustic Emission Signal. It is well known that acoustic emission (AE) is the emission of elastic wave resulting from the deformation and fracture of materials. This study estabished correlation of orthogonal cutting parameter and AE signal, and researched into in-process monitoring of tool wear and failure. The results are as follow; 1. AE RMS was under the influence of cutting speed but hardly influenced by depth of cut and feed rate. 2. AE RMS was under the influence of flank wear. 3. AE count rate increased by increased cutting speed. 4. AE RMS value was rapidly increased in 130 m flank wear.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.43-48
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.938-942
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• 2008

To detect the failures in machine, the signals of current, vibration and acoustic emission are widely used in industry. And unexpected failures of gears are not only extremely damaging but also lead to economic losses. In this paper, to detect the misalignment occurred at between two gears in gearboxes, the signals of current, vibration and AE were measured at gearbox and motor power line. FFT(Fast Fourier Transform) was used for current and vibration signal analysis to find gear failure frequency. Especially, the envelop analysis and wavelet transform were applied for AE signal. Therefore, compared with the results of three kinds of signal, the possibility of earily detection by AE is identified or checked.