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Signal processing and Gaussian neural networks for the edge and damage detection in immersed metal plate-like structures

Signal processing and Gaussian neural networks for the edge and damage detection in immersed... The present study concerns the remote monitoring of immersed plate-like structures as the ones used for marine current turbines. The innovation of this work is the remote damage detection based on a systematic analysis of a small set of ultrasonic measurements limited by the backscattered echoes from the structure edges. The detection and localization are performed by combination of signal processing tools as Hilbert transform, principal component analysis, and thresholding methods and artificial intelligence tools as Gaussian neural networks. The edges of the structure are detected with a Gaussian neural network classifier, and the useful ranges of the measurements are extracted. These ranges are compared with reference signals in order to compute residuals. Finally damage detection is obtained from the magnitude of the residuals. In addition, some geometric parameters such as the incidence angle, the distance between the structure and the emission–reception device, and eventually the damage localization are estimated. The proposed method is validated with laboratory experimental measurements, and the performance is discussed with respect to some significant parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Signal processing and Gaussian neural networks for the edge and damage detection in immersed metal plate-like structures

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References (34)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-016-9464-z
Publisher site
See Article on Publisher Site

Abstract

The present study concerns the remote monitoring of immersed plate-like structures as the ones used for marine current turbines. The innovation of this work is the remote damage detection based on a systematic analysis of a small set of ultrasonic measurements limited by the backscattered echoes from the structure edges. The detection and localization are performed by combination of signal processing tools as Hilbert transform, principal component analysis, and thresholding methods and artificial intelligence tools as Gaussian neural networks. The edges of the structure are detected with a Gaussian neural network classifier, and the useful ranges of the measurements are extracted. These ranges are compared with reference signals in order to compute residuals. Finally damage detection is obtained from the magnitude of the residuals. In addition, some geometric parameters such as the incidence angle, the distance between the structure and the emission–reception device, and eventually the damage localization are estimated. The proposed method is validated with laboratory experimental measurements, and the performance is discussed with respect to some significant parameters.

Journal

Artificial Intelligence Review – Springer Journals

Published: Feb 4, 2016

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