@inproceedings{0ca117b36ee44267a581984fc108d37a,
title = "DeepSHM: A deep learning approach for structural health monitoring based on guided Lamb wave technique",
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.",
keywords = "convolutional neural network (CNN), damage classification, deep learning, Finite-Element-Modelling (FEM), guided Lamb wave, signal processing, Structural Health Monitoring (SHM)",
author = "Vincentius Ewald and Groves, {Roger M.} and Rinze Benedictus",
year = "2019",
doi = "10.1117/12.2506794",
language = "English",
volume = "10970",
series = "SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2019",
publisher = "SPIE",
editor = "Lynch, {Jerome P.} and Hoon Sohn and Kon-Well Wang and Haiying Huang",
booktitle = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019",
address = "United States",
note = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 ; Conference date: 04-03-2019 Through 07-03-2019",
}