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DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique
Author(s): Vincentius Ewald; Roger M. Groves; Rinze Benedictus
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Paper Abstract

In our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals and formalizes a generic method for end-to-end deep learning for SHM. The study case is limited to ultrasonic guided waves SHM. The sensor signal response from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is then fed into the CNN to be trained to obtain the neural weights. In this paper, we present the results of our investigation on CNN complexities that is needed to model the sensor signals based on simulation and experimental testing within the framework of DeepSHM concept.

Paper Details

Date Published: 27 March 2019
PDF: 16 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109700H (27 March 2019); doi: 10.1117/12.2506794
Show Author Affiliations
Vincentius Ewald, Technische Univ. Delft (Netherlands)
Roger M. Groves, Technische Univ. Delft (Netherlands)
Rinze Benedictus, Technische Univ. Delft (Netherlands)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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