Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Tiny Deep Learning Architectures Enabling Sensor-near Acoustic Data Processing and Defect Localization

Version 1 : Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (05:17:33 CEST)

A peer-reviewed article of this Preprint also exists.

Donati, G.; Zonzini, F.; De Marchi, L. Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization. Computers 2023, 12, 129. Donati, G.; Zonzini, F.; De Marchi, L. Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization. Computers 2023, 12, 129.

Abstract

The timely diagnosis of defects at their incipient stage of formation is crucial to extend the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damages, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of Acoustic Emission (AE)-based inspection techniques through the computation of the Time of Arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor Signal-to-Noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative Deep Learning methods are proposed for ToA retrieval, namely a Dilated Convolutional Neural Network (DilCNN) and a Capsule Neural Network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two novel methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

Keywords

Acoustic Emission Monitoring; Capsule Neural Network; Dilated Convolutional Neural 20 Network; Tiny Machine Learning; Time of Arrival Estimation

Subject

Engineering, Electrical and Electronic Engineering

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