Open AccessEmbedded application of convolutional neural networks on Raspberry Pi for SHM
Author(s) -
Monteiro A.,
Oliveira M.,
Oliveira R.,
Silva T.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0877
Subject(s) - convolutional neural network , computer science , structural health monitoring , raspberry pi , microcontroller , field programmable gate array , embedded system , aerospace , digital signal processing , computer hardware , real time computing , artificial intelligence , internet of things , engineering , electrical engineering , aerospace engineering
To date, the authors are not aware of an in‐depth investigation about embedded applications of the convolutional neural network (CNN) algorithm on small, lightweight, and low‐cost hardware (e.g. microcontroller, FPGA, DSP, and Raspberry Pi) applied to detect faults in structural health monitoring (SHM) systems. In this Letter, the authors implement and evaluate both feasibility and performance of an embedded application of the CNN algorithm on the Raspberry Pi 3. The CNN‐embedded algorithm quantifies and classifies dissimilarities between the frames representing healthy and damaged structural conditions. In a case study, the CNN‐embedded application was experimentally evaluated using three piezoelectric patches glued onto an aluminium plate. The results reveal an impressively effective 100% hit rate. This performance may significantly impact the design and analysis of CNN‐based SHM systems where embedded applications are required for identifying structural damage such as those encountered by aerospace structures, rotating machineries, and wind turbines.