A hybrid approach for damage detection and localization on a plate-Like structure

This paper illustrates a hybrid method for identifying structural damage that includes concepts from Physics-Based approaches within a Data-Driven framework. The lessons learned from the use of damage identification methods based on the analysis of modal curvature allow us to develop effective feature engineering in the development of Machine Learning (ML) algorithms. Moreover, the training of the algorithms exploits data provided from a population of damage cases generated by Finite Element (FE) analysis. The above data-analysis pipeline is applied to the identification of damage in a rectangular metallic plate for which measured modal data are available. The damage is modelled as a stiffness reduction over a small area. To have meaningful samples of the training database, the modal convergence of the FE model to the real structure is guaranteed by a structural optimization process. Experimentally identified noise, representative of real-life applications, is then added to the FE results before algorithm training. Damage existence and position are determined by a Novelty Detection approach and a Regression Neural Network, respectively. First, damage is identified on new test cases generated by the same FE procedure used for training. Secondly, the trained algorithms are applied to the experimental dataset. Sensitivity analysis on several parameters (number of samples for training, damage severity and noise levels) is numerically carried out to understand the applicability limits of the present methodology