Combination of wavelet transform and extreme learning machine for detecting damages in composite plates

Abstract

This paper reports a two-stage damage detection method for effectively identifying multi-damage in composite plate structures. The first stage focuses on detecting damage locations using the wavelet transform to reveal the singularities from a modal shape of the composite plate. In the second stage, extreme learning machine (ELM) is employed to search the damage severities from the pre-calculated damage evaluation database (the relationship between the natural frequencies and severities). Using finite element method (FEM) and experimental modal analysis (EMA), the numerical simulations and experimental investigations are executed to verify the effectiveness of the combination method for simply supported composite plates structures with various damages. Results show that the proposed two-stage method is effectively to identify multiple damages in composite plate structures with reasonable precision might be extend to real-world applications.