Automatic Damage Quantification Using Signal Based And Nonlinear Model Based Damage Sensitive Features

Abstract

Structural Health Monitoring (SHM) can be de ned as the process of acquiring and analyzing data from on-board sensors to evaluate the health of a structure. Classically, an SHM process can be performed in four steps: detection, localization, classi cation and quanti cation. This paper addresses damage quanti cation issue as a classi cation problem whereby each class corresponds to a certain damage extent. Starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of nonlinear model based features increases classi cation performance. A support Vector Machine (SVM) is used to perform multi-class classi cation task. Two types of features are used as inputs to the SVM algorithm: Signal Based Features (SBF) and Nonlinear Model Based Features (NMBF). SBF are rooted in a direct use of response signals and do not consider any underlying model of the test structure. NMBF are computed based on parallel Hammerstein models which are identi ed with an Exponential Sine Sweep (ESS) signal. A study of the sensitivity of classi cation performance to the noise contained in output signals is also conducted. Dimension reduction of features vector using Principal Component Analysis (PCA) is carried out in order to nd out if it allows robustifying the quanti cation process suggested in this work. Simulation results on a cantilever beam with a bilinear torsion spring sti ness are considered for demonstration. Results show that by introducing NMBF, classi cation performance is improved. Furthermore, PCA allows for higher recognition rates while reducing features vector dimension. However, classi ers trained on NMBF or on principal components appear to be more sensitive to output noise than those trained on SBF.