• Smart Structures and Systems
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• 제13권1호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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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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• 제5권1호
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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.

• 한국산업융합학회 논문집
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• 제2권2호
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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.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제50권3호
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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)

• 비파괴검사학회지
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• 제22권2호
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• pp.125-131
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• 2002

본 연구에서는 3종류의 취성재료에 비커스 경도기를 사용하여 압축하중에 의한 신호특성과 잔류응력의 에너지 해방에 의하여 검출되는 신호특성을 조사하였다. 각 시험편에서 I영역과 III영역에서는 신호가 검출되었으나, 일정 하중이 유지되는 II영역에서는 음향방출 신호가 전혀 검출되지 않았다. 유리 시험편은 III영역에서 높은 진폭값을 가지는 신호가 검출되었고, $Al_2O_3$ 및 $Al_2O_3/SiC$ 시험편은 I영역에서 높은 진폭값을 가지는 신호가 검출되었다.

• 대한기계학회논문집A
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• 제35권9호
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• pp.1131-1136
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• 2011

본 연구는 자동차용 CNG 연료탱크의 파열시험시 발생하는 음향방출변수의 분석에 관한 내용이다. 연료탱크의 복합재료 중앙부 표면에 150kHz 공진형 AE센서를 부착하여 파열시험시 발생하는 음향방출 신호를 획득하고, 가압유지시간 동안의 hit, amplitude, count, duration, risetime, signal strength 등과 같은 음향방출 변수들을 분석하였다. 음향방출 변수 중 total count, total signal strength가 연료탱크 손상정도를 평가하는데 유용한 변수임을 알 수 있었다.

• 대한기계학회논문집
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• 제12권5호
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• pp.1035-1042
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• 1988

본 연구에서는 단순절삭의 특성과 관련된 신호처리 방법을 검토하고, 선삭의 경우와 달리 제기되는 문제점중의 하나인 센서의 부착위치를 예비실험을 통하여 결정 하며, 공구마멸의 진전에 따른 AE신호의 특성변화를 조사하여 공구마멸 자동감지의 기 초가 되는 공구마멸의 점진적 증가에 따른 AE신호의 특성변화를 관찰하고자 한다. 특히 절삭조건 즉 이송량, 절석깊이, 절삭속도, 그리고 정착된 인서트의 수가 AE신호 의 특성에 미치는 영향을 각각 조사하여 절삭조건의 변화에 따른 AE신호의 변화경향을 살펴보고자 한다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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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.

• 동력기계공학회지
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• 제14권6호
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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.