Bag of visual words based machine learning framework for disbond characterisation in composite sandwich structures using guided waves

Sikdar, Shirsendu and Pal, Joy (2021) Bag of visual words based machine learning framework for disbond characterisation in composite sandwich structures using guided waves. Smart Materials and Structures, 30 (7). ISSN 0964-1726

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Abstract

This paper presents a machine learning framework that uses the bag of visual words (BOVW) for structural health monitoring (SHM) of a composite sandwich structure (CSS) using ultrasonic guided wave (GW) signals. Towards this, experimental analysis of GW propagation in CSS has been carried out for the healthy-state and multiple skin-to-core disbond cases. The registered time-domain signals from the assigned piezoelectric transducer networks on the CSS are converted to time-frequency scalograms by performing a continuous wavelet transform. Eventually, a BOVW based machine learning framework is proposed that uses the speeded-up-robust features for the features extraction and support vector machine for classification of CSSs with and without skin-to-core disbond. The proposed machine learning framework shows its SHM potential to characterise the CSS for healthy and disbond conditions (different locations) with high validation and test accuracy for an unseen dataset. A series of parametric studies are also carried out to analyse the influence of different grid sizes and polynomial order for the proposed framework.

Item Type: Article
Additional Information: Publisher Copyright: © 2021 IOP Publishing Ltd.
Uncontrolled Keywords: bag of visual words,composite sandwich structure,disbond characterisation,guided wave,machine learning,structural health monitoring,support vector machine,signal processing,civil and structural engineering,atomic and molecular physics, and optics,materials science(all),condensed matter physics,mechanics of materials,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1711
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Depositing User: LivePure Connector
Date Deposited: 10 Nov 2021 08:13
Last Modified: 17 Nov 2021 04:48
URI: https://ueaeprints.uea.ac.uk/id/eprint/82005
DOI: 10.1088/1361-665X/ac01a8

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