Frequency-based k-Nearest Neighbors algorithm for electromechanical impedance based detection of damage in adhesive joints

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Tenreiro, AFG (Autor) Outra; António Mendes Lopes (Autor) FEUP; da Silva, LFM (Autor) FEUP

Revista

Título: Applied Soft Computing Journal

Vol. 171

ISSN: 1568-4946

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-ZZ8

DOI: 10.1016/j.asoc.2025.112761

Abstract (EN): Structural adhesive joints are susceptible to many forms of damage that may not be detectable with currently deployed Non-Destructive Tests (NDT). The Electromechanical Impedance Spectroscopy (EMIS) based Structural Health Monitoring (SHM) methodology constitutes an alternative, where a given structure is continuously monitored for damage, thus outperforming NDT. While much has been done in the applicability of EMIS in metallic and composite structures, only preliminary research has been done on the EMIS-based integrity monitoring of adhesive joints. In this paper, two different k-Nearest Neighbor (kNN) approaches are used for both damage detection and damage quantification. Initially, peaks are extracted from the real component of the measured impedance of instrumented adhesive joint specimina, which are then inputted to either a conventional kNN or a novel parallel kNN model, where each individual model is fed with its respective peak information. For void detection, the parallel classifier approach presents a moderately better performance, with an average accuracy of 98% under optimal conditions, but, for damage quantification, a significant improvement in classification is observed. In all cases, the use of the Canberra distance allows for a significant increase in classification accuracy.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18