• Tribology and Lubricants
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• v.18 no.5
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• pp.351-356
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers on the contacting surfaces in real time. The FFT(Fast Fourier Transform) analyses of the AE signal are sued to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.125-137
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• 2009

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.359-360
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• 2008

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.349-365
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• 2018

The strain and acoustic emission (AE) signals of Pocheon granite were measured during uniaxial compression tests to investigate microcrack formation and damage. Crack closure, initiation, and damage stresses of each sample were determined through an analysis of the crack volumetric strain and stiffness. The samples experienced four damage stages according to stress levels: stage 1 = crack closure stage; stage 2 = elastic stage; stage 3 = crack initiation stage; stage 4 = crack damage stage. At least 75% of all AE signals occurred in stages 3 and 4, and different AE parameters were detected in the four stress stages. Rise time, count, energy, and duration clearly showed a tendency to gradually increase with the damage stress stage. In particular, the rise time, energy, and duration increased by at least 95% in stage 4 as compared with stage 1. However, the maximum amplitude showed a smaller increase, and the average frequency decreased slightly at higher stages. These results indicate that as the degree of rock damage increases, the crack size grows larger. The crack types corresponding to the AE signals were determined using the relationship between RA (Rise time / Amplitude) values and average frequencies. Tension cracking was dominant in all stress stages. Shear cracking was rare in stages 1 and 2, but increased in stages 3 and 4. These results are consistent with previous studies that reported cracking begins after samples have already been damaged. Our study shows that the state of rock damage can be investigated solely through an analysis of AE parameters when rocks are under compressive stress. As such, this methodology is suitable for understanding and monitoring the stress state of bedrock.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.254-262
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• 2003

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.27-30
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• 2005

This paper studied the PVDF(polyvinylidene fluoride) and Acoustic Emission sensors characteristics of the laminated composite panels under the low velocity impact. The various impact test by changing impact height is performed on the instrumented drop weight impact tester. The STFT(short time Fourier transform) and WT(wavelet transform) are used to decompose the each sensor signals. A ultrasonic C-scan and digital scope are used to define damaged area in each case. The test result indicated that the individual sensor signals involve the damage initiation and development.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.199-210
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• 2008

Acoustic emission(AE)/Microseimsic(MS) activities are low-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is not easy to determine the precursor and initiation stress level of failure in displacement detection method. To overcome this problem, AE/MS techniques for detection of structure failure and damage have recently adopt in civil engineering. In this study, AE/MS monitoring system, which consist of sensor, data acquisition and operation program, is constructed with domestic technology. To verify and optimize the developed system, we are now carrying out the field application at an underground research laboratory and the developed AE/MS monitoring will be used in detecting of seismic events with various scales.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.4 no.1
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• pp.44-50
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• 2001

Existing surface defects in structural members often act as sites of fatigue crack initiation, and if undetected, these cracks may grow through the thickness of the member, leading to catastrophic failure of the structure. Thus, in-service monitoring of fatigue cracks through reliable and effective nondestructive techniques is an important ingredient in the leak-before-break (LBB) design and safe operation of defects critical structures. An advanced, waveform-based, acoustic emission (AE) technique has been used in this paper to study the characteristics of the signals emanating from the initiation, growth and through-the -thickness penetration of surface fatigue crack in a 6061 aluminum plate. The goal of this experimental study is to determine whether the evolution of the fatigue crocks could be identified from the properties of the waveforms produced during the tests. The AE waveform signals detected at different stages of crack growth was found to have different temporal and spectral characteristics. The data analysis technique presented here can be applied to real-time monitoring of the initiation and propagation of fatigue cracks in structural components.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.2891-2898
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• 2000

The objective of this paper is to develop a fiber-optic acoustic emission(AE) sensor applicable to on-line monitoring systems which is suitable for long-distance signal transmission. An AE sensor was developed by use of a fiber-optic cantilever and an extrinsic Fabry-Perot interferometer(EEPI). The efficiency of signal processing was improved by driving the high frequency AE signals into the low frequency ones. In order to verify the developed sensor, the tensile and the pencil lead fracture(PLF) tests were performed including the experiment showing the Kaiser effect. Form tests, AE signals were successfully detected in the elastic-plastic deformation range, especially higher signals at the crack propagation. The developed sensor was expected to be used for an on-line monitoring of crack propagation in mechanical system.