• Smart Structures and Systems
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• v.13 no.1
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• pp.155-171
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• 2014

Various monitoring techniques are now available for structural health monitoring and Acoustic Emission (AE) is one of them. One of the major advantages of the AE technique is its capability to locate active cracks in structural members. AE crack locating approaches are affected by the signal attenuation and dispersion of elastic waves due to inhomogeneity and geometry of reinforced concrete (RC) members. In this paper, a novel technique is described based on signal processing and sensor arrangement to process multisensory AE data generated by the onset and propagation of cracks and is validated with experimental results from an in-situ load test. Considering the sources of uncertainty in the AE crack location process, a methodology is proposed to capture and locate events generated by cracks. In particular, the relationship between AE events and load is analyzed, and the feasibility of using the AE technique to evaluate the cracking behavior of two RC slab strips during loading to failure is studied.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.56-60
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• 1997

The compressor is one of important elements in refrigeration cycle and play an important role of refrigeration efficiency and quality. This paper analyzes slides in rotary compressors for room air conditioners, monitoring using Acoustic Emission(AE) technique. Reliability of rotary compressors which are factory-tested has been evaluated through visual inspection on taking them apart after long term test, which is life test. This paper describes methods for acquisition and processing of Acoustic Emission(AE) raw signal to monitor state of rotary compressor in Life Test.

• KSTLE International Journal
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• v.5 no.1
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• pp.28-31
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• 2004

The object of the present work is the natural frequency analysis of subambient pressure tri-pad and pico sliders. Head/disk interaction during start/stop and constant speed were detected by using the acoustic emission (AE) test system. The frequency spectrum analysis is performed using the AE signal obtained during the head/disk interaction. The FFT (Fast Fourier Transform) analysis of the AE signals is used to understand the interaction between the AE signal and the state of contact. Natural frequency analysis was performed using the Ansys program. The results indicate acceptable accordance of finite element calculation results with the experimental results.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.147-153
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• 1999

An in-process extraction method of the ground surface roughness is a bottle-neck and essential field in conventional machining process. We define the D.A.R.F(Dimensionless Average Roughness Factor) that has a roughness characteristic of ground surface. D.A.R.F include the absolute average and the standard deviation values which are the analytic parameters of the AE(Acoustic Emission) signal generated during the grinding operation. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.2
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• pp.125-131
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• 2002

we analyzed acoustic emission signals obtained from three kinds of brittle materials under compression load by Vickers hardness tester. The results obtained can be summarized as follows; The signal in each material could be divided into three signal based on the properties of load. All specimens were not detected acoustic emission signals in stage II which was load constant region., and were detected in stage I and stage III. Glass was detected high amplitude signals in stage III. $Al_2O_3\;and\;Al_2O_3/Sic$ were detected high amplitude signals in stage I.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1131-1136
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• 2011

This study is performed on the acoustic emission parameters involved in a burst test for a CNG-vehicle fuel tank. A resonant AE sensor with a central frequency of 150 kHz was attached on the composite materials in the center of the fuel tank. The analysis of AE parameters such as hit, amplitude, count, duration, risetime, and signal strength during load holding was performed. The total count and total signal strength are effective tools for the damage evaluation of a CNG fuel tank.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.5
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• pp.1035-1042
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• 1988

The acoustic emission(AE) signal is monitored in milling operation in order to investigate the relationship between the progressive tool wear and the AE signals. A signal processing technique so called time domain averaging(TDA) is presented for the elimination of the influences of the noise imbedded in the periodic signals. The relationship between the progressive tool wear and the AE signals is investigated by varying the cutting speed, feed, depth of cut and the number of insert. From the measured data, it is observed that the averaged level of the AE signal increases at first with the increase of flank wear to a certain critical value, and then stays almost constant or fluctuates with further increase of the flank wear.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.572-577
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• 2011

The acoustic emission (AE) technique is a well-known non-destructive test technique, both in research and for industrial applications. It is mainly used to monitor the onset of cracking processes in materials and components. Predicting and preventing the crack phenomenon has attracted the attention of many researchers and has continued to provide a large incentive for the use of condition monitoring techniques to detect the earliest stages of cracks. In this research, goal is in grasping features of AE signal caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate feature of divided into three by crack length, the time waveform and the power spectrum were generated by the raw signals and the transferred signal processed by envelope analysis with DWT.

• Journal of Power System Engineering
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• v.14 no.6
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• pp.41-46
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• 2010

Acoustic emission (AE) technique is a well-known non-destructive test technique. Fatigue crack growth test was performed using SM53C to check up the AE signal occurred by crack growth, so AE system was used to detect the crack signal. Features calculated by the AE signals were analyzed to evaluate the steps divided the crack growth into three. The steps, initiation, growth and breaking, were separated by velocity of the crack growth. Time waveform and power spectrum were created by the AE signal of each one of the steps and compared. In the feature domains, it was found that AE values changed rapidly as the velocity of the crack increasing.