Donati, G.; Zonzini, F.; De Marchi, L. Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization. Computers2023, 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.
Donati, G.; Zonzini, F.; De Marchi, L. Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization. Computers2023, 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
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
