• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.2
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• pp.114-119
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• 2016

This paper dealt with the propagation characteristics of acoustic signal in insulation oil for the purpose of improving the reliability of AE (acoustic emission) method used for condition monitoring of oil-immersed transformers. A discharge source was placed in insulation oil and AE sensors ($f_c$ :140 kHz) were attached on the oil tank to study the changes of velocity and propagation path with the depth and distance. The average velocity was 1,436 m/s and the velocity decreased with the increase of depth from the oil surface to 430 mm. The propagation paths were classified into three sections by the shortest reflection path of the detected signal. The minimum distinguishable distance in each section was 70 mm. It was also verified that PD (partial discharge) with a magnitude over than 500 pC can be detected by the AE sensors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.259-264
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• 1995

Recently, in order to achieve high flexibilty, monitoring and control strategies of a new type have been developed. This paper investigates the fesability of using scoustic emission signal analysis for the detection of built-up edge during machining. Results for maching SM45C steel show that the presence of a built-up edge can significantil affect the generation of acoustic emission in metal cutting. When the cutting speed comes to the conditions conducive to development of built-up edge, it is shown that the slope of curve-fitted AErms signal undergoes a change. The fesability of utilizing AErms in built-up edge sensing is sugested.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.4
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• pp.61-67
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• 1999

In turning the chip may be produced in the form of continuous chip or discontinuous one. Continuous chips produced at high speed machining may hit the newly cut workpiece surface and adversely affect the appearance of the surface finish and may interfere with tool and sometimes induce tool fracture. In this study relationship between AE signal and chip form was experimentally investigated, The experimental results show that types of chip form are possible to be classified from the AE signal using fuzzy logic.

• Smart Structures and Systems
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• v.8 no.3
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• pp.321-341
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• 2011

Acoustic emission (AE) monitoring was conducted for mortar specimens under three types of static loading patterns (cubic-splitting, direct-shear and pull-out). Each of the applied loading patterns was expected to produce a particular fracture process. Subsequently, the AEs generated by various fracture or damage processes carried specific information on temporal micro-crack behaviors of concrete for post analysis, which was represented in the form of detected AE signal characteristics. Among various available characteristics of acquired AE signals, frequency content was of great interest. In this study, cement-based piezoelectric sensor (as AE transducer) and home-programmed DEcLIN monitoring system were utilized for AE monitoring on mortar. The cement-based piezoelectric sensor demonstrated enhanced sensitivity and broad frequency domain response range after being embedded into mortar specimens. This broad band characteristic of cement-based piezoelectric sensor in frequency domain response benefited the analysis of frequency content of AE. Various evaluation methods were introduced and employed to clarify the variation characteristics of AE frequency content in each test. It was found that the variation behaviors of AE frequency content exhibited a close relationship with the applied loading processes during the tests.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.203-211
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• 1998

It is well recognized recently that acoustic emission, which is an elastic wave generated from rapid release of elastic energy in steressed solids, is very useful tool for on-line monitoring of microscopic behavior of deformation of material. In this study, three point bend test was performed to evaluate the microscopic damage progress during the loading and failure mechanism of mortar beam by monitoring the characteristic of AE signal. The relationship between AE characteristic and microscopic failure mechanism is discussed. In addition 2 dimensional AE source location based on triangular method was also done to monitor the intiation and propagation of micro crack around notch tip of mortar beam. It was shown that AE source location was very effective to predict the growth behavior of micro crack in mortar beam specimen.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.51-58
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• 2010

Recently, aerospace structures have lightweight trend in order to reduce the cost of fuel and system, Carbon Fiber Reinforced Plastic (CFRP) can give the ability to reduce weight at 20~50% as the substitution of metal alloy, and there are advantages such as high Non-rigid, specific strength and anti-corrosion, but it is difficult to prove its destruction properties due to heterogeneous structure and anisotropy. In this study we designed specimen, inducing distinguishing destructions of material (for example, matrix crack, fiber breakage, and delamination) by using the Carbon Fiber Reinforced Plastic (CFRP) which is used in a real aircraft, to apply acoustic emission technique to aerospace structures. And we gained data via tensile testing and acoustic emission technique, from which each fault signal was classified respectively by using AE parameters and waveform.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.13-25
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• 2020

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

• Nuclear Engineering and Technology
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• v.47 no.4
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• pp.454-460
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• 2015

Acoustic emission (AE) is one of the promising methods for detecting the formation of stress corrosion cracks (SCCs) in laboratory tests. This method has the advantage of online inspection. Some studies have been conducted to investigate the characteristics of AE parameters during SCC propagation. However, it is difficult to classify the distinct features of SCC behavior. Because the previous studies were performed on slow strain rate test or compact tension specimens, it is difficult to make certain correlations between AE signals and actual SCC behavior in real tube-type specimens. In this study, the specimen was a AISI 304 stainless steel tube widely applied in the nuclear industry, and an accelerated test was conducted at high temperature and pressure with a corrosive environmental condition. The study result indicated that intense AE signals were mainly detected in the elastic deformation region, and a good correlation was observed between AE activity and crack growth. By contrast, the behavior of accumulated counts was divided into four regions. According to the waveform analysis, a specific waveform pattern was observed during SCC development. It is suggested that AE can be used to detect and monitor SCC initiation and propagation in actual tubes.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.261-266
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• 2004

This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.