Data-driven structural health monitoring using feature fusion and hybrid deep learning

Viet Hung, Dang, Tran-Ngoc, Hoa, Nguyen, Tung. V, Bui-Tien, Thanh, De Roeck, Guido and Nguyen, Huan X. ORCID: https://orcid.org/0000-0002-4105-2558 (2020) Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Transactions on Automation Science and Engineering . ISSN 1545-5955 [Article] (Published online first) (doi:10.1109/TASE.2020.3034401)

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Abstract

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 31283
Notes on copyright: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Huan Nguyen
Date Deposited: 29 Oct 2020 11:21
Last Modified: 09 Jun 2021 20:33
URI: https://eprints.mdx.ac.uk/id/eprint/31283

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