Structural Damage Detection Using Vibration Response Through Cross-Correlation Analysis: Experimental Study

AIAA Journal ◽  
2018 ◽  
Vol 56 (6) ◽  
pp. 2455-2465 ◽  
Author(s):  
C. M. Diwakar ◽  
N. Patil ◽  
Mohammed Rabius Sunny
Keyword(s):  
Experimental Study ◽  
Correlation Analysis ◽  
Damage Detection ◽  
Structural Damage ◽  
Cross Correlation ◽  
Vibration Response ◽  
Structural Damage Detection ◽  
Cross Correlation Analysis

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

A numerical–experimental study for structural damage detection in CFRP plates using remote vibration measurements

2017 ◽  
Vol 8 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Guilherme Ferreira Gomes ◽  
Yohan Alí Diaz Mendéz ◽  
Sebastião Simões da Cunha ◽  
Antônio Carlos Ancelotti
Keyword(s):  
Experimental Study ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Vibration Measurements

scholarly journals Cointegration for structural damage detection under environmental variabilities: An experimental study

2020 ◽  
Author(s):  
Juan Carlos Burgos Díaz ◽  
Bilal Ali Qadri ◽  
Martin Dalgaard Ulriksen
Keyword(s):  
Experimental Study ◽  
Damage Detection ◽  
Structural Damage ◽  
Environmental Variability ◽  
Characteristic Curve ◽  
Structural Damage Detection ◽  
Detection Scheme ◽  
Positive Ratio ◽  
Damage Indices ◽  
Heating Cable

An intricacy in vibration-based structural damage detection (VSDD) relates to environmental variabilities imposing limitations to the damage detectability. One method that has been put forth to resolve the issue is cointegration. Here, non-stationary vibration features are linearly combined into stationary residuals, which are then employed as damage indices under the assumption that the non-stationarity is governed by environmental variabilities. In the present paper, the feasibility of using cointegration to mitigate environmental variabilities while retaining sensitivity to damage is examined through an experimental study with a steel beam. A temperature-based environmental variability is introduced to the beam by use of a heating cable, while damage is emulated by adding local mass perturbations. The vibration response of the beam in different environmental and structural states is captured and utilized as features in a cointegration-based damage detection scheme. The performance of the scheme is assessed and compared to that of a scheme not accounting for the variability on the basis of the false positive ratio (FPR), the true positive ratio (TPR), and the area under the receiver operating characteristic curve (AUC). The results show that cointegration effectively mitigates the temperature variability and allows for an improved damage detectability compared to that of the scheme without a mitigation strategy

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.

scholarly journals Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility

Materials ◽  
2017 ◽  
Vol 10 (8) ◽  
pp. 866 ◽  
Author(s):  
Yun-Lai Zhou ◽  
Hongyou Cao ◽  
Quanmin Liu ◽  
Magd Abdel Wahab
Keyword(s):  
Correlation Analysis ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection
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