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Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)154-170
Number of pages17
JournalJournal of Sound and Vibration
Volume388
Early online date9 Nov 2016
DOIs
Publication statusPublished - Feb 2017
Publication typeA1 Journal article-refereed

Abstract

Abstract Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Keywords

  • Vibration, Structural health monitoring, Structural damage detection, Neural networks, Convolutional neural networks

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