Embedded application of convolutional neural networks on Raspberry Pi for SHM. Issue 11 (1st May 2018)

• Record Type: Journal Article
• Authors: Monteiro, A.; de Oliveira, M.; de Oliveira, R.; da Silva, T.
• Is Part Of: Electronics letters. Volume 54:Issue 11(2018)
• Page Start: 680
• Page End: 682
• Publication Date: 2018-05-01
• Subjects: microcontrollers -- neural nets -- condition monitoring -- wind turbines -- structural engineering -- plates (structures); convolutional neural networks -- convolutional neural network algorithm -- structural health monitoring systems -- CNN algorithm -- Raspberry Pi 3 -- classifies dissimilarities -- healthy conditions -- damaged structural conditions -- CNN‐embedded application -- design -- SHM systems; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0877
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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.