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    Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

    Source: Journal of Pressure Vessel Technology:;2005:;volume( 127 ):;issue: 003::page 294
    Author:
    Piervincenzo Rizzo
    ,
    Ivan Bartoli
    ,
    Alessandro Marzani
    ,
    Francesco Lanza di Scalea
    DOI: 10.1115/1.1990213
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.
    keyword(s): Pipes , Artificial neural networks , Signals , Reflection , Wavelets , Waves , Inspection , Noise (Sound) , Ultrasonic waves , Product quality , Steel AND Measurement ,
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      Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/132505
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    contributor authorPiervincenzo Rizzo
    contributor authorIvan Bartoli
    contributor authorAlessandro Marzani
    contributor authorFrancesco Lanza di Scalea
    date accessioned2017-05-09T00:17:35Z
    date available2017-05-09T00:17:35Z
    date copyrightAugust, 2005
    date issued2005
    identifier issn0094-9930
    identifier otherJPVTAS-28457#294_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/132505
    description abstractThis paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDefect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing
    typeJournal Paper
    journal volume127
    journal issue3
    journal titleJournal of Pressure Vessel Technology
    identifier doi10.1115/1.1990213
    journal fristpage294
    journal lastpage303
    identifier eissn1528-8978
    keywordsPipes
    keywordsArtificial neural networks
    keywordsSignals
    keywordsReflection
    keywordsWavelets
    keywordsWaves
    keywordsInspection
    keywordsNoise (Sound)
    keywordsUltrasonic waves
    keywordsProduct quality
    keywordsSteel AND Measurement
    treeJournal of Pressure Vessel Technology:;2005:;volume( 127 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian