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Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks

Rautela, M and Gopalakrishnan, S (2020) Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks. In: Expert Systems with Applications . (In Press)

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Official URL: https://dx.doi.org/10.1016/j.eswa.2020.114189

Abstract

The inverse problem of damage identification involves real-time, continuous observation of structures to detect any undesired, abnormal behavior and ultrasonic guided waves are considered as one of the preferred candidates for this. A parallel implementation of a reduced-order spectral finite element model is utilized to formulate the forward problem in an isotropic and a composite waveguide. In this work, along with a time-series dataset, a 2D representation of continuous wavelet transformation based time�frequency dataset is also developed. The datasets are corrupted with several levels of Gaussian random noise to incorporate different kinds of uncertainties and noise present in the real scenario. Deep learning networks like convolutional and recurrent neural networks are utilized to numerically approximate the solution of the inverse problem. A hybrid strategy of classification and regression in a supervised setting is proposed for combined damage detection and localization. The performance of the networks is compared based on metrics like accuracy, loss value, mean absolute error, mean absolute percentage error, and coefficient of determination. The predictions from conventional machine learning algorithms, trained on feature engineered dataset are compared with the deep learning algorithms. The generalization of the trained deep networks on different excitation frequencies and a higher level of uncertainties is also highlighted in this work. © 2020 Elsevier Ltd

Item Type: Journal Article
Publication: Expert Systems with Applications
Publisher: Elsevier Ltd
Additional Information: Copyright to this article belongs to Elsevier Ltd
Keywords: Convolution; Convolutional neural networks; Damage detection; Guided electromagnetic wave propagation; Inverse problems; Learning algorithms; Learning systems; Structural analysis; Ultrasonic applications; Ultrasonic waves, Coefficient of determination; Continuous observation; Continuous wavelet transformation; Mean absolute percentage error; Parallel implementations; Spectral finite elements; Structural damage detection; Ultrasonic guided wave, Recurrent neural networks
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 22 Dec 2020 11:01
Last Modified: 22 Dec 2020 11:01
URI: http://eprints.iisc.ac.in/id/eprint/67222

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