contributor author | Piervincenzo Rizzo | |
contributor author | Ivan Bartoli | |
contributor author | Alessandro Marzani | |
contributor author | Francesco Lanza di Scalea | |
date accessioned | 2017-05-09T00:17:35Z | |
date available | 2017-05-09T00:17:35Z | |
date copyright | August, 2005 | |
date issued | 2005 | |
identifier issn | 0094-9930 | |
identifier other | JPVTAS-28457#294_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/132505 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing | |
type | Journal Paper | |
journal volume | 127 | |
journal issue | 3 | |
journal title | Journal of Pressure Vessel Technology | |
identifier doi | 10.1115/1.1990213 | |
journal fristpage | 294 | |
journal lastpage | 303 | |
identifier eissn | 1528-8978 | |
keywords | Pipes | |
keywords | Artificial neural networks | |
keywords | Signals | |
keywords | Reflection | |
keywords | Wavelets | |
keywords | Waves | |
keywords | Inspection | |
keywords | Noise (Sound) | |
keywords | Ultrasonic waves | |
keywords | Product quality | |
keywords | Steel AND Measurement | |
tree | Journal of Pressure Vessel Technology:;2005:;volume( 127 ):;issue: 003 | |
contenttype | Fulltext | |