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A damage classification approach for structural health monitoring using machine learning

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Tibaduiza Burgos, Diego Alexander
Torres-Arredondo, Miguel Ángel
Vitola Oyaga, Jaime
Anaya Vejar, Maribel
Pozo Montero, FrancescMés informacióMés informacióMés informació
Document typeArticle
Defense date2018-12-02
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.
CitationTibaduiza, D.A., Torres-Arredondo, M.A., Vitola, J., Anaya, M., Pozo, F. A damage classification approach for structural health monitoring using machine learning. "Complexity", 2 Desembre 2018, vol. 2018, p. 1-14. 
URIhttp://hdl.handle.net/2117/125815
DOI10.1155/2018/5081283
ISSN1076-2787
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  • Departament de Matemàtiques - Articles de revista [2.979]
  • CoDAlab - Control, Modelització, Identificació i Aplicacions - Articles de revista [254]
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