Void detection in structural adhesive joints using a k-Nearest Neighbors model with features from Electromechanical Impedance Spectroscopy

Abstract

Electromechanical Impedance Spectroscopy (EMIS) is a promising Structural Health Monitoring (SHM) method which allows the early detection of defects by analyzing the structure response to an AC electrical signal swept through a range of high frequencies. In this work, EMIS measurements of pristine and damaged adhesive joints were performed, and
features were extracted from the experimental measurements. These features were inputted to a k-Nearest Neighbors (kNN) model for damage
detection. Results show that only one type of features is enough for damage detection. Furthermore, the use of the Manhattan Distance in the kNN enables a better classification.