• Geomechanics and Engineering
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• v.12 no.4
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• pp.627-637
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• 2017

The high positive correlation between plastic strain of loaded coal-rock and AE (acoustic emission) characteristic parameter was studied and proved through AE experiment during coal-rock uniaxial compression process. The results show that plastic strain in the whole process of uniaxial compression can be gained through the experiment. Moreover, coal-rock loaded process can be divided into four phases through analyzing the change of the plastic strain curve : pressure consolidation phase, apparent linear elastic phase, accelerated deformation phase, rupture and development phase, which corresponds to conventional elastic-plastic change law of loaded coal-rock. The theoretical curve of damage constitutive model is in high agreement with the experimental curve. So the damage evolution law of coal rock damage can be indicated by both acoustic emission and plastic strain. The results have great academic and realistic significance for further study of both AE signal characteristics during loaded coal-rock damaged process and the forecasting of coal-rock dynamic disasters.

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• Journal of the Korean Society for Railway
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• v.4 no.3
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.63-68
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• 2004

The high pressure vessels that are constructed by welding process have many welding lines and most of the leakage defects are occurred on these welding lines. The acoustic emission(AE) technique has adopted to detect the defect location and leakage on welding parts, but the AE signal in leakage are incomplete due to the attenuation, reiteration, instability and limit of defect size. To overcome these troubles, the experiments in this study are conducted to measure the indirect media-propagated AE signal perpendicular to the leakage hole. The AE signals that are acquired from the direct and indirect media are analyzed, and the reliability of the indirect media-propagated AE signal are examined experimentally. By AE signal investigation, this method can be adopted to detect efficiently the leakage in welding parts.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.10-17
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• 2008

The purpose of this thesis is to reduce of error for source location through acoustic emission(AE) signal, generated elastic wave from crack growth to leak for facility diagnosis. Especially, in order to overcome noise from original signal, this paper proposed enhancement of source location by using noise reduction based on wavelet transform. To evaluate actual performance in experiments, Pencil Lead Break is used crack signal source on the aluminum plate and drain valve of air compressor is used as substitute pressure vessel to generate leak signal. In signal processing, wavelet shrinkage and soft threshold are used to discriminate signal source and then source location techniques have been effectively used with group velocity using material property and time difference between sensor using cross correlation. Source location for crack and leak test have some difference, but the result show that improved 30% with a average length within 10.46mm in crack test and improved 2% compare with average filter in leak test when we applied wavelet transform.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.480-487
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• 2003

To improve environmental control in various plants, signal characteristics of plants have been studied by a nondestructive technique. In this paper, the acoustic emission (AE) from plants was analyzed for water stress dependency. AE signals were taken from gymnosperm and angiosperm. AE sensor detected AE signals from the plant stem underneath the plant surface below the sensor. AE hit-event counts in daytime were more than those in night time, and it was found that the daily hit counts pattern was strongly affected by the water stress in the plant. frequency bands of AE signals from the angiosperm was different from those from the gymnosperm. Frequency bands of AE in outdoor condition were in accord with those in indoor having similar conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.4
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• pp.14-25
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• 2010

Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena became the cause of not only defective product but also damage of a press mold. In order to solve this problem AE(Acoustic emission) system was introduced. AE system was expected to be very effective to real time detection of the defective product and for the prevention of the damage in the press molds In this study, for the pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For the analysis and processing the AE signals picked in real time from the normal or the detective products, specialized software called AE-win(software for processing AE signal from Physical Acoustics Corporation) was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight of the press and thickness of sheet and process type.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Composites Research
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• v.24 no.3
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• pp.25-30
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• 2011

The studies on the non-destructive testing methods of the composite materials are very important for improving their reliability and safety. AE(Acoustic Emission) can evaluate the defects by detecting the emitting strain energy when elastic waves are generated by the generation and growth of a crack, plastic deformation, fiber breakage, matrix cleavage or delamination. In this paper, the AE signals of the filament wound composite cylinder and sandwich cylinder during the pressure test were measured and analyzed. The signal characteristics of PVDF sensors were measured, and an AE signal analyzer which had the band-pass filter and L-C resonance filter were designed and fabricated. Also, the crack detection capability of the fabricated AE signal analyzer wes evaluated during the pressure tests of the filament wound composite cylinder and the sandwich cylinder.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.198-204
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• 2003

A basic experiment was performed to control tile plant growth using acoustic emission technique considering the environmental conditions for plant. At a dry soil condition, the signals due to the cavitation from xylem of angiosperm and gymnosperm were mainly detected. The strong signal from xylem and the weak signal from plasmodesmata and casparian strip were detected at the same time after distilled water was provided. Two signals after providing tile acid and distilled water were contrary to each other. The wind, number of leaf, music, temperature and humidity affected the acoustic emission count from plants but the frequency ranges of the detected signals were the same.