• Title/Summary/Keyword: AE pattern

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Proposition and Application of Novel DWT Mother Function for AE signature (AE 신호를 위한 새로운 DWT 기저함수 제안 및 적용)

  • Gu, Dong-Sik;Kim, Jae-Gu;Choi, Byeong-Keun
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2011.04a
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    • pp.582-587
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    • 2011
  • Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

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Non-destructive evaluation and pattern recognition for SCRC columns using the AE technique

  • Du, Fangzhu;Li, Dongsheng
    • Structural Monitoring and Maintenance
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    • v.6 no.3
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    • pp.173-190
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    • 2019
  • Steel-confined reinforced concrete (SCRC) columns feature highly complex and invisible mechanisms that make damage evaluation and pattern recognition difficult. In the present article, the prevailing acoustic emission (AE) technique was applied to monitor and evaluate the damage process of steel-confined RC columns in a quasi-static test. AE energy-based indicators, such as index of damage and relax ratio, were proposed to trace the damage progress and quantitatively evaluate the damage state. The fuzzy C-means algorithm successfully discriminated the AE data of different patterns, validity analysis guaranteed cluster accuracy, and principal component analysis simplified the datasets. A detailed statistical investigation on typical AE features was conducted to relate the clustered AE signals to micro mechanisms and the observed damage patterns, and differences between steel-confined and unconfined RC columns were compared and illustrated.

Detection and Classification of Defect Signals from Rotator by AE Signal Pattern Recognition (AE 신호 형상 인식법에 의한 회전체의 신호 검출 및 분류 연구)

  • Kim, Ku-Young;Lee, Kang-Yong;Kim, Hee-Soo;Lee, Hyun
    • 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..

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Acoustic Emission Studies on the Structural Integrity Test of Welded High Strength Steel using Pattern Recognition (패턴인식을 이용한 고장력강의 용접 구조건전성 평가에 대한 음향방출 사례연구)

  • Kim, Gil-Dong;Rhee, Zhang-Kyu
    • Proceedings of the Safety Management and Science Conference
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    • 2008.04a
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    • pp.185-196
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    • 2008
  • The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

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A Perceptual Study on the Temporal Cues of English Intervocalic Plosives for Various Groups Depending on Background Language, English Listening Ability, and Age (언어별, 연령별, 수준별 집단에 의한 모음간 영어 파열음 유/무성 인지 연구)

  • Kang, Seok-Han
    • Speech Sciences
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    • v.13 no.2
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    • pp.133-145
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    • 2006
  • In order to understand the various groups' perceptual pattern in both VCV trochee and iambus, this study examined the identification correctness and cue robustness for the unit intervals in light of background language, age, and English listening ability. The 4 groups of Native Speakers of English, Korean College Students of High Listening Achievement, Korean College Students of Low Listening Achievement, and Korean Elementary Students took part in the experiments. Tokens of $/d{\ae}per,\;d{\ae}per,\;d{\ae}per,\;d{\ae}per,\;d{\ae}per,\;d{\ae}per$ in trochee and of $/{\eth}{\partial}\;p{\ae}d,\;{\eth}{\partial}\;b{\ae}d,\;{\eth}{\partial}\;t{\ae}d,\;{\eth}{\partial}\;d{\ae}d,\;{\eth}{\partial}\;k{\ae}d,\;{\eth}{\partial}\;g{\ae}d/$ in iambus were extracted and modified into experimental signals composed of two digits(voiced-1, voiceless-0) by following the temporal intervals, in which the signals consisted of preceding vowel, closure, VOT, and post-vowel. In the first experiment of identification correctness in VCV iambus environment, all groups showed almost 100% correctness rate, while in trochee environment all groups were different(native speaker 87%, college high 74%, college low 70%, elementary 65%). In the second experiment of cue robustness, all groups showed the similar perceptual pattern in both environments. There was the order of robustness cues in VCV trochee: pre-vowel ${\gg}$ closure ${\gg}$ VOT ${\gg}$ post-vowel, while the order in VCV iambus: VOT ${\gg}$ post-vowel ${\gg}$ closure ${\gg}$ pre-vowel. In some condition, however, we found moderately different perceptual pattern depending on language, age and listening level.

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Acoustic Emission Studies on the Structural Integrity Test of Welded High Strength Steel using Pattern Recognition: Focused on Tensile Test (패턴인식을 이용한 고장력강의 용접 구조건전성 평가에 대한 음향방출 사례연구: 인장시험을 중심으로)

  • Kim, Gil-Dong;Rhee, Zhang-Kyu
    • Journal of the Korea Safety Management & Science
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    • v.10 no.4
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    • pp.127-134
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    • 2008
  • The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Monotonic simple tension and AE tests were conducted against the 3 kinds of welded specimen. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multi-variate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

Discrimination of Acoustic Emission Signals using Pattern Recognition Analysis (형상인식법을 이용한 음향방출신호의 분류)

  • Joo, Y.S.;Jung, H.K.;Sim, C.M.;Lim, H.T.
    • Journal of the Korean Society for Nondestructive Testing
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    • v.10 no.2
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    • pp.23-31
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    • 1990
  • Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

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Chip Shape Control using AE Signal in Pure Copper Turning (순동선삭가공에서 AE 신호를 이용한 칩 형상 제어)

  • Oh, Jeong Kyu;Kim, Pyeong Ho;Koo, Joon Young;Kim, Duck Whan;Kim, Jeong Suk
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.23 no.4
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    • pp.330-336
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    • 2014
  • The continuous chip generated in cutting process deteriorates workpiece, tool, and machine tool system. It is necessary to treat this continuous chip in ductile material machining condition for stable cutting. This paper deals with the chip control method using acoustic emission(AE) signal in pure copper turning operation. AE raw signals, root mean square(RMS) signals and wavelet transformed signals measured in turning process are introduced to analysis for chip patterns. With analysis of AE signals, it is obtained that the produced chip patterns are correlated with the specified AE signals which are transformed by fuzzy pattern algorithm. By this experimental investigation, the chip patterns can be classified at significant level in pure copper machining process and controlled from continuous chips to reduced-length stable chips.

Acoustic emission technique to identify stress corrosion cracking damage

  • Soltangharaei, V.;Hill, J.W.;Ai, Li;Anay, R.;Greer, B.;Bayat, Mahmoud;Ziehl, P.
    • Structural Engineering and Mechanics
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    • v.75 no.6
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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.

Recognition of damage pattern and evolution in CFRP cable with a novel bonding anchorage by acoustic emission

  • Wu, Jingyu;Lan, Chengming;Xian, Guijun;Li, Hui
    • Smart Structures and Systems
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    • v.21 no.4
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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.