• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.328-332
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• 1996

The compressor is one of important elements in refrigerator cycle and play an important role of refrigeration efficiency and quality. Therefore it is very important to monitor state of normal and abnormal compressor. In this research, technic of AE is used for monitoring abnormal rotary compressor and an AE parameter which is a most proper parameter to monitor the abnormal state of compressor is determined by signal processing, Finally, the monitoring result of rotary compressor is agreement with the result of life test.

• 한국신뢰성학회지:신뢰성응용연구
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• 제13권2호
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• pp.129-140
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• 2013

As a fundamental experimental study for reliability improvement of lithium ion secondary battery, degradation mechanism was investigated by microscopic observation and acoustic emission monitoring. Microstructural observation of the decomposed battery after cycle test revealed mechanical and chemical damages such as interface delamination, microcrack of the electrodes, and solid electrolyte interphase (SEI). Acoustic emission (AE) signal was detected during charge and discharge of lithium ion battery to investigate relationships among cumulative count, discharge capacity, and microdamages. With increasing number of cycle, discharge capacity was decreased and AE cumulative count was observed to increase. Observed damages were attributed to sources of the detected AE signals.

• Nuclear Engineering and Technology
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• 제35권1호
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• pp.25-34
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• 2003

The research on the development of the fault monitoring system for the thermal reduction reactor has been performed preliminarily in order to support the successful operation of the thermal reduction reactor. The final task of the development of the fault monitoring system is to assure the integrity of the thermal$_3$ reduction reactor by the acoustic emission (AE) method. The objectives of this paper are to identify and characterize the fault-induced signals for the discrimination of the various AE signals acquired during the reactor operation. The AE data acquisition and analysis system was constructed and applied to the fault monitoring of the small- scale reduction reactor, Through the series of experiments, the various signals such as background noise, operating signals, and fault-induced signals were measured and their characteristics were identified, which will be used in the signal discrimination for further application to full-scale thermal reduction reactor.

• 한국해양공학회지
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• 제15권2호
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• pp.61-65
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• 2001

The fracture toughness of ceramics can be measure by such various methods as DT (double torsion), CN (chevron notch) etc. But, the application of these methods to the engineering ceramics is very difficult because of its very high hardness. So, IF (indentation fracture) method is generally used for the evaluation of fracture toughness of ceramics. The Median crack induced by the sharp Vickers indenter was compared with the detected AE (acoustic emission) signal. On the silicon nitride ceramics, the AE test results agree fairly well with the median crack occurance and growth process. But, on the alumina, very many complicated crack signals were detected besides median crack. It can be considered that the IF methods must be used in limited engineering ceramics materials.

• Structural Engineering and Mechanics
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• 제75권6호
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• pp.723-736
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• 2020

In this paper, acoustic emission (AE) and pattern recognition are utilized to identify the AE signal signatures caused by propagation of stress corrosion cracking (SCC) in a 304 stainless steel plate. The surface of the plate is under almost uniform tensile stress at a notch. A corrosive environment is provided by exposing the notch to a solution of 1% Potassium Tetrathionate by weight. The Global b-value indicated an occurrence of the first visible crack and damage stages during the SCC. Furthermore, a method based on linear regression has been developed for damage identification using AE data.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.15-20
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• 1999

In this study, the effects of the maximum grit depth of cut on the grinding characteristics were investigated. And AE signals produced during grinding processes have been studied to find out the appropriate AE parameters for assessing grinding processes. S45C steel has been ground under the conditions yielding removal rate of workpiece, 100, 200, 300 and 400rnm$^3$/min which was achived by altering workpiece velocity($\upsilon$) and apparent depth of cut(Z). According to the experimental results, the value of surface roughness increases but grinding power, energy rate of AE signal(AErms$^2$) and specific grinding energy consumed decrease with increase of the maximum grit depth of cut.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2002년도 추계학술발표대회 논문집
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• pp.151-154
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• 2002

The objective of this study is to find the change of signal characteristics of matrix cracks due to the different specimen shapes. As the concept of the smart structure, monitoring of acoustic emission (AE) can be applied to inspect the fracture of the structures in operating condition using built-in sensors. To understand the characteristics of matrix crack signals, we performed tensile tests by changing the thickness and width of the specimens. This paper describes the implementation of time-frequency analysis such as wavelet transform (WT) fur the quantitative evaluation of fracture signals. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.293-298
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• 2003

This study deals with the source location method of defect which can be occurred in structure. The existing methods was very difficult to be applied to predict it because of using very complex numerical formula. The triangulation method which was proposed in his study can predict the source location predicted easily with small amount of data. Wave arrival time data can be directly converted into source-sensor distance is known. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. For the consideration of jointed part in structure, the source location method was applied to the welded workpiece. The signal propagation velocity was measured in welding part for the purpose of application to the part and the revised algorithm of source location was proposed.

• 대한기계학회논문집
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• 제17권7호
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Smart Structures and Systems
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• 제21권4호
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• pp.421-433
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• 2018

Carbon fiber reinforced polymer (CFRP) cable has good mechanical properties and corrosion resistance. However, the anchorage of CFRP cable is a big issue due to the anisotropic property of CFRP material. In this article, a high-efficient bonding anchorage with novel configuration is developed for CFRP cables. The acoustic emission (AE) technique is employed to evaluate the performance of anchorage in the fatigue test and post-fatigue ultimate bearing capacity test. The obtained AE signals are analyzed by using a combination of unsupervised K-means clustering and supervised K-nearest neighbor classification (K-NN) for quantifying the performance of the anchorage and damage evolutions. An AE feature vector (including both frequency and energy characteristics of AE signal) for clustering analysis is proposed and the under-sampling approaches are employed to regress the influence of the imbalanced classes distribution in AE dataset for improving clustering quality. The results indicate that four classes exist in AE dataset, which correspond to the shear deformation of potting compound, matrix cracking, fiber-matrix debonding and fiber fracture in CFRP bars. The AE intensity released by the deformation of potting compound is very slight during the whole loading process and no obvious premature damage observed in CFRP bars aroused by anchorage effect at relative low stress level, indicating the anchorage configuration in this study is reliable.