A Tiny Machine Learning Approach to the Edge Localization of Acoustic Sources via Convolutional Neural Networks

Source localization is a critical step in Acoustic Emission (AE)-based Structural Health Monitoring (SHM), since it allows to identify the point of a structure where most of the acoustic activity is growing due to both ageing (e.g., cracks, delamination, etc.) and sudden flaws. Recently, Artificial Intelligence (AI) algorithms have been proposed, which can overcome standard statistical methods especially when the signal-to-noise ratio is poor. In this work, the embodiment of tiny Convolutional Neural Network (CNN) models on a 32-bit microcontroller unit is presented for the task of Time of Arrival (ToA) estimation, which is the crucial parameter to be estimated for AE localization. Experimental results on real-field data prove that the embedded models can achieve satisfying accuracy for AE identification.