Structural damage detection using auto correlation functions of vibration response under sinusoidal excitation

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

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How Many Vibration Response Sensors for Damage Detection & Localization on a Structural Topology? An Experimental Exploratory Study

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.569-570.791 ◽

2013 ◽

Vol 569-570 ◽

pp. 791-798

Author(s):

Christos S. Sakaris ◽

John S. Sakellariou ◽

Spilios D. Fassois

Keyword(s):

Damage Detection ◽

Exploratory Study ◽

Structural Damage ◽

Vibration Response ◽

Truss Structure ◽

Damage Localization ◽

Structural Topology ◽

Structural Damage Detection

The number of vibration response sensors required for structural damage detection andprecise localization on a continuous structural topology is investigated. For damage detection thestate–of–the–art of vibration based methods need a required number of sensors q that may be “low”compared to the number of structural modes m, that is q << m. Yet, the opposite is generally suggestedfor precise damage localization, that is q > m. In this study the hypothesis that a “low” numberof vibration response sensors, q << m, may, under certain conditions, suffice for precise damage localization,is postulated. This hypothesis is “proven” experimentally by demonstrating that preciselocalization is indeed possible using a single vibration response sensor and an advanced StructuralHealth Monitoring methodology on a laboratory 3D truss structure.

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Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses

Journal of Sound and Vibration ◽

10.1016/j.jsv.2012.09.027 ◽

2013 ◽

Vol 332 (4) ◽

pp. 645-653 ◽

Cited By ~ 10

Author(s):

Le Wang ◽

Zhichun Yang

Keyword(s):

Damage Detection ◽

Correlation Functions ◽

Structural Damage ◽

Detection Method ◽

Excitation Frequency ◽

Response Type ◽

Structural Damage Detection ◽

Frequency Range ◽

Vibration Responses

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MINOR DAMAGE DETECTION USING CHAOTIC EXCITATION AND RECURRENCE ANALYSIS

Journal of Earthquake and Tsunami ◽

10.1142/s1793431111001054 ◽

2011 ◽

Vol 05 (03) ◽

pp. 259-270 ◽

Cited By ~ 3

Author(s):

TADANOBU SATO ◽

YOUHEI TANAKA

Keyword(s):

System Dynamics ◽

Damage Detection ◽

Structural Damage ◽

Vibration Response ◽

Detection Technique ◽

Structural Damage Detection ◽

Recurrence Analysis ◽

Numerical Example ◽

Response Data

In this paper, we propose a new attractor-based structural damage detection technique using chaotic excitation. Attractor is reconstructed using vibration response data and sensitive to the change of the system dynamics. By comparing the change of attractors from healthy and damaged structures, we detect and localize the damage. We use recurrence analysis to analyze the change of attractor. Numerical example demonstrates the robustness and sensitivity of the proposed method.

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