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Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems

Authors
  • Miorelli, Roberto
  • KULAKOVSKYI, ANDRII
  • Chapuis, Bastien
  • D’Almeida, Oscar
  • Mesnil, Olivier
Publication Date
May 01, 2021
Identifiers
DOI: 10.1016/j.ultras.2021.106372
OAI: oai:HAL:hal-03163639v1
Source
HAL
Keywords
Language
English
License
Unknown
External links

Abstract

This paper presents the use of a kernel-based machine learning strategy targeting classification and regression tasks in view of automatic flaw(s) detection, localization and characterization. The studied use-case is a structural health monitoring configuration with an array of piezoelectric sensors integrated on aluminum panels affected by flaws of various positions and dimensions. The measured guided wave signals are post processed with a guided wave imaging algorithm in order to obtain an image representing the health of each specimen. These images are then used as inputs to build classification and regression models. In this paper, an extensive numerical validation campaign is conducted to validate the process. Then the inversion is applied to an experimental campaign, which demonstrate the ability to use a numerically-built model to invert experimental data.

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