Structural damage detection using cross correlation functions of vibration response

2010 ◽  
Vol 329 (24) ◽  
pp. 5070-5086 ◽  
Author(s):  
Le Wang ◽  
Zhichun Yang ◽  
T.P. Waters
Keyword(s):  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Vibration Response ◽  
Structural Damage Detection

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

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.

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

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.

scholarly journals Experimental investigations on the cross-correlation function amplitude vector of the dynamic strain under varying environmental temperature for structural damage detection

2018 ◽  
Vol 39 (3) ◽  
pp. 631-649
Author(s):  
Miao Li ◽  
Wei-Xin Ren ◽  
Tian-Li Huang ◽  
Ning-Bo Wang
Keyword(s):  
Correlation Function ◽  
Damage Detection ◽  
Temperature Effect ◽  
Structural Damage ◽  
Cross Correlation ◽  
Environmental Temperature ◽  
Cross Correlation Function ◽  
Dynamic Strain ◽  
Experimental Investigations ◽  
Structural Damage Detection

This article focuses on the experimental investigations on the cross-correlation function amplitude vector of the dynamic strain (CorV_S) under varying environmental temperature for structural damage detection. It is verified that under white noise excitation, CorV_S is only related to the natural frequencies, mode shapes, and damping ratios of structures. The normalized CorV_S of the undamaged structure maintains a uniform shape. A laboratory experimental investigation based on an end-fixed steel beam shows that CorV_S can be used for structural damage detection. However, CorV_S constructed by the dynamic strain of in-situ test varies with time, and the CorV_S curves do not have the same shape. When the environmental temperature fluctuates significantly, high correlation exists between the dynamic strain and environmental temperature. By analyzing the power spectral density of the signals measured from active and inactive strain gauges, it is found that the signals induced by temperature stress, which do not reflect the dynamic performance of the bridge, exist in the very low-frequency band. To avoid the interference to CorV_S, the temperature effect component is separated from the dynamic strain by analytical mode decomposition method. Then, each CorV_S curve maintains a uniform shape. The results demonstrate that it is prone to get a misjudgment for the condition of a structure if temperature effect on CorV_S is ignored. It is necessary to eliminate the environmental temperature effect on CorV_S for the damage detection of a structure in service.

Structural Damage Detection Using Auto/Cross-Correlation Functions Under Multiple Unknown Excitations

2014 ◽  
Vol 14 (05) ◽  
pp. 1440006 ◽  
Author(s):  
Pinghe Ni ◽  
Yong Xia ◽  
Siu-Seong Law ◽  
Songye Zhu
Keyword(s):  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Detection Methods ◽  
Structural System ◽  
Vibration Responses ◽  
Structural System Identification

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

MINOR DAMAGE DETECTION USING CHAOTIC EXCITATION AND RECURRENCE ANALYSIS

2011 ◽  
Vol 05 (03) ◽  
pp. 259-270 ◽  
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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