Component-wise damage detection by neural networks and refined FEs training

Abstract

Multilayer perceptrons are utilized in this work for vibration-based damage detection of multi-component aerospace structures. A back-propagation algorithm is utilized along with Monte Carlo simulations and advanced structural theories for training Artificial Neural Networks (ANN’s), which are able to detect and classify local damages in structures given the natural frequencies and the associated vibrations modes. The latter ones are feed into the network in terms of Modal Assurance Criterion (MAC), which is a scalar representing the degree of consistency between undamaged and damaged modal vectors. Dataset and ANN training process is carried out by means of Carrera Unified Formulation (CUF), according to which refined finite elements with component-wise capabilities can be implemented in a hierarchical and unified manner. The proposed results demonstrate that CUF-trained ANNs can approximate complete mapping of the damage distribution, even in case of low damage intensities and local defects in localized components (stringers, spar caps, webs, etc.).