Electromechanical Impedance Data Fusion for Damage Detection

Abstract

In this paper results of application of electromechanical impedance (EMI) method for damage assessment in composite panel is presented. Artificial damage simulated by additional mass and real impact damage are investigated. Large number of measurements is analyzed and possibility of utilization of machine learning approach for clustering the damage cases is validated. Clustering is based on principal components obtained based on conductance spectra. Machine learning methods based on K-nearest neighbor, neural networks and K-means are utilized. Moreover, popular damage indices RMSD and CCD were also utilized.