• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.308-315
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• 2009

Because the attenuation of AE signal in composite materials is relatively higher than that of metallic materials, it is required to develop a damage assessment technique less affected by the attenuation property of composite materials in order to use AE sensing as a damage detection method. In the signal processing procedure, it is profitable to use the leading wave that arrives first because the leading wave is less influenced by the boundary conditions. Using wavelet transform, we investigated the frequency characteristics of impact induced AE signals focused on the leading wave in advance and chose the key factors to discriminate the damaged condition quantitatively. In this research, we established a damage assessment technique using the sharing percentage of the wavelet detail components of AE signal, and conducted a low-velocity impact test on composite laminates to confirm the feasibility of the proposed signal processing method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.765-771
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• 2001

Elastic-plastic fracture toughness JIC can be used a s an effective design criterion in elastic-plastic fracture mechanics. Among the JIC test methods approved by ASTM, unloading compliance method was used in this study. In order to examine the relationship between fracture behavior of JIC test and AE signals, the post processing of AE signals has been carried out by Short Time Fourier Transform(STFT), one of the time-frequency analysis methods. The objective of this study is to evaluate the application of characterization of AE signals for unloading compliance method of JIC test. As a result of time-frequency analysis, we could extract the AE from the raw signal and analyze the frequencies in AE signal at the same time. AE signal generated by elastic-plastic fracture of material has some different aspects at elastic and plastic ranges, or the first portion of crack growth by fracture. First of all, increased energy recorded and detected by using AE count method increase rapidly from the start of ductile fracture. The variation of main frequency range with time-frequency analysis method could be confirmed. We could know fracture behavior of interior material by examination AE characteristics generated in real-time when elastic-plastic fracture occurred in material under loading.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.46-53
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• 2001

The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1867-1869
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• 2003

In this paper, chamber(Circuit breaker compartment of C-GIS) made of stainless steel with 4 mm width is used. Artificial defect was made on enclosure or HV conductor of chamber and $SF_6$ gas was injected into it according to pressure. In this experiment, Acoustic emission sensors of different types was used to compare sensitivity to detect acoustic signal occurred by Partial discharge(PD) of according to types and resonance frequency in $SF_6$ gas atmosphere. Sensors used in tests was R6I, R15I and 2/4/6 Pre-Amplifier connected with R6IU without pre. amp. In case of R6IU, gain was adjusted with 40 dB like other sensors and operated by differential mode. Post amplifier(post. amp) and band pass filter(BPF) were developed Gain of post. amp. is 60 dB and BPF has band width of $50{\sim}300$ kHz. Also, envelope circuit developed reduces frequency of AE sensor. As a result, in $SF_6$ atmosphere, R6IU and R6I had resonance frequency of 60 Hz was better than R15I. Also, R6IU was better than R6I because of type property of pre.amp. had differential mode.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.39-45
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• 2001

Fourier transform has been one of the most common tools to study the frequency characteristics of signals. With the Fourier transform alone, however, it is difficult to tell whether signal's frequency contents evolve in time or not. Except for a few special cases, the frequency contents of most signals encountered in the real world change with time. Time-frequency analysis methods are developed recently to overcome the drawbacks of Fourier transform, which can represent the information of signals in time and frequency at the same time. In this study, damage process of a cross-ply carbon fiber reinforced plastic (CFRP) under monotonic tensile loading was characterized by acoustic emission. Different kinds of CFRP specimens were used to determine the characteristics of AE signals. Time-frequency analysis methods were employed for the analysis of fracture mechanisms in CFRP such as mix cracking, debonding, fiber fracture and delamination.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2328-2335
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• 2002

A hybrid composite material has many potential usage due to the high specific strength and the resistance to fatigue, when compared to other composite materials such as fiber reinforced plastic(FRP) and metal matrix composite(MMC). However, the fracture mechanism of hybrid composite material is extremely complicated because of the bonding structure of metals and FRP. In this study, Al 7075 sheets and carbon epoxy preprags were used to fabricate the hybrid composite. Recently, nondestructive technique has been used to evaluate the fracture mechanism of these composite materials. AE technique was used to clarify the microscopic damage behavior and failure mechanism of A17075/CFRP hybrid composite. It was found that AE paralneters such as AE event, energy and amplitude were effective to evaluate the failure process of Al 7075/CFRP composite. In addition, the relationship between the AE signal and the characteristics of fracture surface using optical microscope was discussed.

• Journal of Welding and Joining
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• v.33 no.4
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• pp.7-16
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• 2015

New lasers such as high power, high brightness and short wavelength laser are using diverse industry. Also new technologies are developing actively to solve various issues such as spattering, process monitoring, deep penetration and key-hole stability. ICALEO is the international congress where recent technology for laser material processing and laser system are present. At 2014, it was held at San Diego in USA and more than 260 papers were presented from 28 country. The effect of the laser beam shape such as Gaussian like and top-hat was investigated on acoustic emission signal and pore formation in welding. Inline penetration depth was measured with ICI(Inline Coherent Imaging) technique and the data was verified with real time X-ray image on laser welding. The laser welding performance at low pressure environment was evaluated for the thick plate alloy steel. UV laser was used to weld various metals such as Cu, Aluminum, steel and stainless steel. The effect of the wavelength of the laser on the formation of the wave at the wall of the key-hole front and the absorptivity was investigated.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.252-257
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• 1988

This paper discribes an improved technique for locating partial discharge sites within operating transformers by ultrasonic method which utilizes the different travel times of the electrical and ultrasonic signals produced by partial discharges to determine the location of the sources. The technique was to develope the measurement system based on the enhancement of the acoustic emission signals using signal square, circuit to improve the detectable sensitivity and reliability. Also an ultrasonic pulse generator was designed and made to transmit periodically pulses equivalent to pratial discharges and to check the good performance of the system, echo effects and self-diagnosis. Through the laboratory work, it was confirmed that the system and the generator can be used satisfactorily for diagnosing power transformers and for studying echo effects and self-diagnosis of the system.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.44-51
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• 2000

In this paper, using a wide-band AE sensor with the frequency range from 100[kHz] to 1.5[kHz], the frequency spectra of AE signals generated from the corona discharges of the needle-plane gap and from the partial discharges of an epoxy void were analyzed to determine the proper ultrasonic sensor with optimum frequency range according to the patterns of corona discharges. We also examined the propagation characteristics of AE signals in oil and the relationship between the magnitude of corona discharge and the magnitude of AE signals in peak-to-peak value under the application of 60[Hz] ac high-voltage. From these results, the main frequency spectra of AE signals emitted from the corona discharges of the needle-plane gap were about 130[kHz] by the fast fourier transform, but the main frequency spectra appeared to be 230[kHz] in the partial discharges of an epoxy void. The magnitude of AE signals was proportional to the magnitude of corona discharge and discharge current pulse with increasing the applied voltages.