• 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 for Nondestructive Testing
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• v.20 no.6
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• pp.481-489
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• 2000

A time-frequency analysis method was developed to analyze the dispersive waves caused by impact loads in structural members such as beams and plates. Stress waves generated by ball drop and pencil lead break were recorded by ultrasonic transducers and acoustic emission (AE) sensors. Wavelet transform (WT) using Gabor function was employed to analyze the dispersive waves in the time-frequency domain, and then to find the arrival time of the waves as a function of frequency. The measured group velocities in the beam and the plate were compared with the predictions based on the Timoshenko beam theory and Rayleigh-Lamb frequency equations, respectively. The agreements were found to be very good.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.3
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• pp.174-183
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• 1996

Acoustic emission(AE) response during stress corrosion cracking(SCC) of Inconel 600 alloy has been monitored to study the AE detectability of crack generation and growth by comparing the crack behavior with AE parameters processed, and to evaluate the applicability as a nondestructive evaluation(AE) by measuring the minimum crack size detectable with AE. Variously heat-treated specimens were tensioned by constant extension rate test(CERT) in various extension rate to give rise to the different SCC behavior of specimens. The AE amplitude level generated from intergranular stress-corrosion cracking(IGSCC) is higher than those from ductile fracture and mechanical deformation, which means the AE amplitude can be a significant parameter for distinguishing the An source. AE can also provide the effective means to identify the transition from the small crack initiation and formation of dominant cracks to the dominant crack growth. Minimum crack size detectable with AE is supposed to be approximately 200 to $400{\mu}m$ in length and below $100{\mu}m$ in depth. The test results show that AE technique has a capability for detecting the early stage of IGSCC growth and the potential for practical application as a NDE.

• Journal of Adhesion and Interface
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• v.6 no.2
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• pp.13-20
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• 2005

Interfacial shear strength (IFSS) of environmentally friendly natural fiber reinforced polymer composites plays a very important role in controlling the overall mechanical performance. The IFSS of various Ramie and Kenaf fibers/epoxy composites was evaluated using the combination of micromechanical test and nondestructive acoustic emission (AE) to find out optimal conditions for desirable final performance. Dynamic contact angle was measured for Ramie and Kenaf fibers and correlated the wettability properties with interfacial adhesion. Mechanical properties of Ramie and Kenaf fibers were investigated using single-fiber tensile test and analyzed statistically by both uni-and bimodal Weibull distributions. An influence of clamping effect on a real elongation for both Ramie and Kenaf fibers were evaluated as well. Two different microfailure modes, axial debonding and fibril fracture coming from fiber bundles and single fiber composites (SFC) were observed under tension and compression.

• Proceedings of the KIEE Conference
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• 1997.07e
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• pp.1761-1763
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• 1997

Because of internal voids in insulators give rise to partial discharge (PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and reduction of the insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission (AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation, We analyzed the PD pulse and AE pulses by regression analysis, compared to these obtained the correlation coefficient and determination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. This is in agreement with the results of the research by Yoshimura and Fujita.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1936-1938
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• 2000

In this paper, a frequency spectra of AE (acoustic emission) signals detected from the partial discharges of an epoxy resin void and a cast resin transformer in operating were analyzed to offer the proper frequency range of AE signals from the corona discharge for the purpose of AE sensor selection, From these results, a frequency spectra of AE signals emitted from the corona discharges in the void of an epoxy resin sample were about 190[kHz] to 220[kHz] by the FFT(fast fourier transform), A frequency spectra of AE signals emitted from a cast. resin transformer with non-load were appeared to be downward of about 140[kHz] by the FFT, and then a frequency spectra of AE signals emitted from the above of cast resin transformer with load were appeared to increase from about 190[kHz] to 220[kHz] by the FFT.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Composites Research
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• v.28 no.1
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• pp.22-27
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• 2015

Although it is important to have high strength of each of fiber and matrix, interface between fiber and matrix is most important. If NaCl water penetrates the interface, that area will be weak. So experiment about increasing interfacial strength is in process. In this study, the change of properties by mechanical, interfacial and micromechanical tests was observed after NaCl and aging treatment. The changes in mechanical properties of glass fiber were investigated using single-fiber tensile test. Interfacial properties between glass fiber and epoxy resin were evaluated using nondestructive acoustic emission (AE) and micromechanical test applied to fatigue test. Through change of fatigue properties, relative interfacial properties were evaluate. In conclusion, glass fiber diameter decreased and the reduction of mechanical and interfacial was observed with NaCl solution and aging treatment.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.476-486
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• 2004

This paper is the second part of the study on the development of a smart active layer (SAL) sensor, which consists of two parts. As mentioned in the first paper, structural health monitoring (SHM) is a new technology that is being increasingly applied at the industrial field as a potential approach to improve cost and convenience of structural inspection. Recently, the development of smart sensor is very active for real application. This study has focused on preparation and application study of SAL sensor which is described with regard to the theory and concept of the SAL sensor in the first paper. In order to detect elastic wave, smart piezoelectric sensor, SAL, is fabricated by using a piezoelectric element, shielding layer and protection layer. This protection layer plays an important role in a patched network of distributed piezoelectric sensor and shielding treatment. Four types of SAL sensor are designed/prepared/tested, and these details will be discussed in the paper In this study, SAL sensor ran be feasibly applied to perform structural health monitoring and to detect damage sources which result in elastic waves.