Vibration‐based damage localization with load vectors under temperature changes

Bhuyan, Delwar Hossain; Gautier, Guillaume; Touz, Nicolas Le; Döhler, Michael; Hille, Falk; Dumoulin, Jean; Mevel, Laurent

doi:10.1002/stc.2439

Cited by 19 publications

(10 citation statements)

References 47 publications

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“…These tasks can be addressed based on damage‐sensitive features that are defined in the reference (healthy) state of the system and that are evaluated for damage diagnosis during the operating states of the structure. Since the considered features are computed from vibration measurements, they may be affected by noise and changing environmental conditions, like variable temperature or excitation 2–4 . When not accounted for, those changes may lead to false alarms, a characteristic that impedes their application for structural monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Reviews of the early developments of vibration‐based damage detection can be found in previous studies 29–31 . The performance of these methods is conditioned on several factors, for example, the quality of parameters estimated from the data, the availability of large amount of training data from different damage scenarios, 26 the ability to track the selected parameter estimates after identifying them from the healthy state of the structure, 32 and the ability to account for changing environmental conditions like the temperature, 4 amongst other factors.…”

Section: Introductionmentioning

confidence: 99%

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Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance

Greś

Döhler

Andersen³

et al. 2021

Struct Control Health Monit

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Summary In the context of detecting changes in structural systems, several vibration‐based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. These methods involve computing data‐based features, which are then evaluated in statistical tests to detect damages. While being sensitive to damages, the data‐based features are affected by changes in the ambient excitation properties that potentially lead to false alarms in the statistical tests, a characteristic that renders their use impractical for structural monitoring. In this paper, a damage detection method is presented that is robust to changes in the covariance of the ambient excitation. The proposed approach is based on the Mahalanobis distance of output covariance Hankel matrices, which are normalized with respect to possibly changing excitation properties. The statistical properties of the developed damage feature are reported and used for efficient hypothesis testing. Its robustness towards changes in the excitation covariance is illustrated on numerical simulations and successfully tested on a numerical offshore foundation model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance

Greś

Döhler

Andersen³

et al. 2021

Struct Control Health Monit

Self Cite

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“…These variabilities, which are governed by the temperature, wind speed, humidity, excitation level, and so forth, are known to camouflage damaged-induced changes in the feature set and thus deteriorate the damage detectability [3]. Several studies have noted that the temperature constitutes the most influential parameter [4,5,6,7,8,9], and therefore we limit this paper to investigate the effect of temperature-induced variability.…”

Section: Introductionmentioning

confidence: 99%

Damage detection under temperature variability using closed-loop mode shapes

Qadri¹,

Kristensen²,

Ulriksen³

2020

Preprint

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The dynamic characteristics of any structural system depend on the temperature. This poses a challenge in vibration-based damage detection, as temperature variability can mask damage-induced shifts in the vibration features. Different means for resolving the issue have been put forth, and two general method types can be distinguished; (i) those mitigating the effect of temperature variability on the features and (ii) those increasing the sensitivity to damage of the features. The present paper explores the use of features composed of closed-loop (CL) mode shapes, which combine attributes from both method groups by offering adequate sensitivity to damage and robustness to temperature variability. The CL mode shapes are designed using an eigenstructure assignment scheme formulated as a bi-objective optimization problem. The first objective is the reciprocal of the spectral norm of the CL mode shape Jacobian matrix, which is thus to be minimized to maximize the sensitivity to damage. The second objective, whose implementation hinges on the assumptions that temperature variability induces spatially uniform stiffness changes and that homogeneous sensing is employed, is a measure of how much the damping in each of the assigned CL modes deviates from a classical distribution. Since classically damped mode shapes obtained using homogeneous sensing are invariant under spatially uniform stiffness changes, the latter objective is minimized to promote robustness to temperature variability. The designed CL mode shape features can be used in any damage detection method, but in the paper we restrict the use to outlier analysis and assess the merit of the proposed scheme in the context of numerical examples. The damage detection results are compared to findings obtained using cointegration (a well-established method for mitigating the effect of temperature variability), and it is seen how the proposed scheme outperforms the cointegration-based method.

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“…Structural physical parameters will be altered due to structural damage, which will result in a difference in the corresponding dynamic characteristics (e.g., natural frequency, mode curvature, and strain mode), through which structural damage can be identified [6][7][8][9]. Several studies have focused on damage identification based on structural global responses.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Zhang

Tang

Zhou

et al. 2020

Sensors

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Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

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Vibration‐based damage localization with load vectors under temperature changes

Cited by 19 publications

References 47 publications

Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance

Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance

Damage detection under temperature variability using closed-loop mode shapes

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

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