Journal article
Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing
Journal of pressure vessel technology, v 127(3), pp 294-303
01 Aug 2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing
- Creators
- Piervincenzo Rizzo - University of California San DiegoIvan Bartoli - University of California San DiegoAlessandro Marzani - University of BolognaFrancesco Lanza di Scalea - University of California San Diego
- Publication Details
- Journal of pressure vessel technology, v 127(3), pp 294-303
- Publisher
- ASME
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000232264000014
- Scopus ID
- 2-s2.0-24944513857
- Other Identifier
- 991020547611604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Mechanical