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Damage Quantification of High-Rate Impacts Using Hybrid Deep Learning Models


 The ability to classify the damage of a structure caused by high-rate, high-acceleration impacts has been of scientific interest for many years. These dynamic events are often nonstationary with broadband spectral energy, making damage classification in this setting significantly more challenging. Recently, structural health monitoring (SHM) has benefitted from data-driven techniques, such as deep learning, that can overcome unclear system dynamics. This paper presents a hybrid damage classification method that fuses deep, shallow, unsupervised, and supervised machine learning (ML) algorithms, and classifies the damage levels of a structure. A deep unsupervised Convolutional Variational Autoencoder (CVAE) first extracts features containing the structure’s damage information. A shallow supervised support vector machine (SVM) classifier then uses the extracted damage information to predict the structure’s damage level. This hybrid damage classification method is applied to a highly instrumented electronic assembly subjected to high-rate high-acceleration drop tower impacts. Using the extremely limited and sparse data collected from these experiments, the hybrid SHM method infers the electronic assembly’s damage level with an accuracy of 73.0% and an F1 score of 72.9%. This technique can also be extended to remaining useful life (RUL) prediction.