• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.572-577
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• 2011

The acoustic emission (AE) technique is a well-known non-destructive test technique, both in research and for industrial applications. It is mainly used to monitor the onset of cracking processes in materials and components. Predicting and preventing the crack phenomenon has attracted the attention of many researchers and has continued to provide a large incentive for the use of condition monitoring techniques to detect the earliest stages of cracks. In this research, goal is in grasping features of AE signal caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate feature of divided into three by crack length, the time waveform and the power spectrum were generated by the raw signals and the transferred signal processed by envelope analysis with DWT.

• KSTLE International Journal
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• 제6권1호
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• pp.17-20
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• 2005

The object of the present work is the natural lre%uency analysis of femto slider, HeaHdisk interaction during starustop and constant speed were detected by using the acoustic emission (AE) test system. The frequency spectrum analysis wasperformed using the AE signal obtained during the head/disk interaction. The FFT (Fast Fourier Transform) analysis of the AEsignals is used to understand the interaction between the AE signal and the state of contact. Natural frequency analysis wasperformed using the ANSYS program. The results indicate acceptable accordance of finite element calculation results with theexperimental results.

• KSTLE International Journal
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• 제5권1호
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• pp.28-31
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• 2004

The object of the present work is the natural frequency analysis of subambient pressure tri-pad and pico sliders. Head/disk interaction during start/stop and constant speed were detected by using the acoustic emission (AE) test system. The frequency spectrum analysis is performed using the AE signal obtained during the head/disk interaction. The FFT (Fast Fourier Transform) analysis of the AE signals is used to understand the interaction between the AE signal and the state of contact. Natural frequency analysis was performed using the Ansys program. The results indicate acceptable accordance of finite element calculation results with the experimental results.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.361-364
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• 1995

Wavelet Transform is a new tools for signal processing, such as data compressing extraction of parameter for Reconition and Diagnostics. This transform has an advandage of a good resolution compared to Fast Fourier Transform (FFT) In this study, we employ the wavelet transform for analysis of Acoustic Emission raw signal generated form rotary compressor. In abnormal condition of rotary compressor, the state of operating condition can be classified by analizing coefficient of wavelet transformed signal.

• 한국생산제조학회지
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• 제6권4호
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• 한국안전학회지
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• 제38권2호
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• pp.36-43
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• 2023

In this study, a four-point bending test was conducted to assess and detect the damage to reinforced concrete structures using the acoustic emission (AE) technique. Based on the crack investigation results, flexural failure was classified into four stages and compared with the characteristic analysis results of AE parameters. The parametric characterization indicated that the activity of the primary AE signal was high in the early stage, and that of the second signal increased after the flexural cracks stabilized. Because the secondary AE signal included noise generated by friction, parameter-based analysis for damage assessment was performed using the primary signal; the secondary signal was used as complement. The activity analyses of the primary and secondary signals effectively classified crack propagation; however, determining the macrocracks and yielding of reinforcing bars had certain limitations. Nevertheless, applying the damage index with cumulative AE energy is a complementary technique for detecting and assessing structure damage that well detects the occurrence of macrocracks.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.881-882
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• 2008

This paper presents an algorithm of target detection of an underwater acoustic signal by estimating the background. At first, subtract the estimated background from the underwater acoustic signal. To estimate the background, this paper uses an algorithm of Denoising. By using Thresholding and Power analysis, we extract targets from the signal to eliminate the background. The proposed method is valuable as an algorithm to reduce calculation amounts of multi frames we will apply.

• 한국콘텐츠학회논문지
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• 제4권2호
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• pp.1-6
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• 2004

본 논문에서는 음란 유해 사이트에서 성행위 묘사시 발생하는 음향 신호를 처리하고 분석하여 표준 패턴 음향신호와 어느 정도 일치하는지 상관 계수를 계산함으로써 내용에 기반하여 음란콘텐츠를 차단하는 방법에 대해 제안하고자 한다. 기존의 음란 유해 사이트 차단 방법은 목록기반과 단어기반에 의한 방법이어서 새로이 생겨나는 음란사이트가 차단이 안되거나 음란사이트 운영자가 교묘히 음란 단어를 변경함으로써 단어 기반으로 음란사이트가 차단이 안되는 문제가 존재했었다. 이를 해결하기 위해 본 논문에서는 콘텐츠기반의 차단법을 다루고자 한다. 특히 본 논문은 음란물의 콘텐츠(내용)에 기반한 전체 시스템중 음향 신호처리에 기반 한 음란 사이트 차단법에 대해 다루고자 한다. 끝으로 실험에 의해 제안한 방법의 유용성을 입증하고자 한다.

• 말소리와 음성과학
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• 제3권3호
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• pp.99-108
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• 2011

The perturbation parameters like jitter, shimmer, and signal-to-noise ratio (SNR) are largely estimated in the particular segment from the subjective or whole portion of the given pathological voice signal although there are many possible regions to be able to analyze the voice signals. In this paper, the pathological voice signals were classified as type 1, 2, 3, or 4 according to narrow band spectrogram and the value differences of the perturbation parameters extracted in the subjective and entire portion tended to be getting bigger as from type 1 to type 4 signals. Therefore, sample selection method based on moving window to analyze type 2 and 3 signals as well as type 1 signals is proposed. Although type 3 signals cannot be analyzed using the perturbation analysis, the type 3 signals by selecting out the samples in which error count is less than 10 through moving window were analyzed. At present, there is no method to be able to analyze the type 4 signals. Future research will endeavor to determine the best way to evaluate such voices.