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

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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.

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Authors and Affiliations

  1. Groupe de Recherche en Electronique et Automatique du Havre, GREAH, Normandie Université, Université Le Havre, 75, rue Bellot, 76600, Le Havre, France

    Y. Sidibe, F. Druaux & D. Lefebvre

  2. Laboratoire Ondes et Milieux Complexes, LOMC UMR CNRS 6294, Normandie Université, Université Le Havre, 75, rue Bellot, 76600, Le Havre, France

    G. Maze & F. Léon

Authors
  1. Y. Sidibe
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  2. F. Druaux
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  3. D. Lefebvre
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  4. G. Maze
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  5. F. Léon
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Corresponding author

Correspondence to D. Lefebvre.

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Sidibe, Y., Druaux, F., Lefebvre, D. et al. Signal processing and Gaussian neural networks for the edge and damage detection in immersed metal plate-like structures. Artif Intell Rev 46, 289–305 (2016). https://doi.org/10.1007/s10462-016-9464-z

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  • DOI: https://doi.org/10.1007/s10462-016-9464-z

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