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

Research output: Contribution to journalArticleScientificpeer-review

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Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. / Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

In: Journal of Sound and Vibration, Vol. 388, 02.2017, p. 154-170.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Abdeljaber, O, Avci, O, Kiranyaz, S, Gabbouj, M & Inman, DJ 2017, 'Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks', Journal of Sound and Vibration, vol. 388, pp. 154-170. https://doi.org/10.1016/j.jsv.2016.10.043

APA

Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154-170. https://doi.org/10.1016/j.jsv.2016.10.043

Vancouver

Author

Abdeljaber, Osama ; Avci, Onur ; Kiranyaz, Serkan ; Gabbouj, Moncef ; Inman, Daniel J. / Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. In: Journal of Sound and Vibration. 2017 ; Vol. 388. pp. 154-170.

Bibtex - Download

@article{6402ed6efa8e4480a521084d8b85e17c,
title = "Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks",
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",
author = "Osama Abdeljaber and Onur Avci and Serkan Kiranyaz and Moncef Gabbouj and Inman, {Daniel J.}",
note = "EXT={"}Kiranyaz, Serkan{"}",
year = "2017",
month = "2",
doi = "10.1016/j.jsv.2016.10.043",
language = "English",
volume = "388",
pages = "154--170",
journal = "Journal of Sound and Vibration",
issn = "0022-460X",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

AU - Abdeljaber, Osama

AU - Avci, Onur

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

AU - Inman, Daniel J.

N1 - EXT="Kiranyaz, Serkan"

PY - 2017/2

Y1 - 2017/2

N2 - 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.

AB - 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.

KW - Vibration

KW - Structural health monitoring

KW - Structural damage detection

KW - Neural networks

KW - Convolutional neural networks

U2 - 10.1016/j.jsv.2016.10.043

DO - 10.1016/j.jsv.2016.10.043

M3 - Article

VL - 388

SP - 154

EP - 170

JO - Journal of Sound and Vibration

JF - Journal of Sound and Vibration

SN - 0022-460X

ER -