Stationarity‐Based Approach for the Selection of Lag Length in Cointegration Analysis Used for Structural Damage Detection

Dao, Phong B.; Staszewski, Wiesław J.; Klepka, Andrzej

doi:10.1111/mice.12238

Cited by 20 publications

(18 citation statements)

References 50 publications

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“…A rigorous explanation on how to compute β is outside the scope of the present paper, so the reader is referred to [16] for such details. We note that the order/number of lags of the VECM model used in the present example is set to 13, which is found by using the method proposed by Dao et al [44]. The obtained cointegration residual can be seen in Fig.…”

Section: Comparison With An Existing Eov Mitigation Methodsmentioning

confidence: 97%

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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Section: Comparison With An Existing Eov Mitigation Methodsmentioning

confidence: 97%

Damage detection under temperature variability using closed-loop mode shapes

Qadri¹,

Kristensen²,

Ulriksen³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…From each test, the most stationary cointegrated residual and its associated cointegrated vector β β β i are found. These cointegrated vectors β β β i , i ∈ [1, v], are the ones that give the most stationary cointegrated residuals in the defined ranges of q and k. In this study, the range of values to be used for k is defined according to the procedure described in [14], which is also used for q, thus…”

Section: Cointegration-based Schemementioning

confidence: 99%

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

Díaz¹,

Qadri²,

Ulriksen³

2020

Preprint

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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

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“…A variety of modal identification techniques either in time or frequency domain have been proposed, including stochastic subspace identification (SSI), eigensystem realization algorithm (ERA), and frequency domain decomposition (FDD) . The obtained modal properties are further utilized in various structural health monitoring applications, such as finite element model updating, damage detection, and cable tension estimation . As such, modal analysis is a key component in many civil engineering applications that require the dynamic characteristics of a structure.…”

Section: Introductionmentioning

confidence: 99%

Automated peak picking using region‐based convolutional neural network for operational modal analysis

Kim

Sim

2019

Struct Control Health Monit

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Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses.One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region-based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy.parameters of the physical modes are consistently found regardless of the provided system orders. 15 The modal parameters calculated from SSI or ERA with different system orders are grouped based on their similarity to each other, from which the frequently obtained are selected as the physical modes. [16][17][18][19] Neu et al. 20 and Cabboi et al. 21 further improved the approach in terms of automation by minimizing user-defined parameters. In addition, Afshar and Khodaygan 22 presented an enhanced stabilization chart for automated modal analysis. However, the identification performance is still highly dependent on the maximum system order, of which an optimal value should be provided as a priori information.Peak picking associated with frequency domain approaches has also been used to carry out automated modal analysis. [23][24][25] Peak picking on the frequency domain representation is considered as an intuitive way for operational modal analysis, as natural modes generally have salient peaks. A general approach for automated peak picking in the literature is to select maximum values of frequency domain representations of the structural responses (e.g., Fourier transform and power spectrum) from each of the user-defined subfrequency ranges, which are believed to include peaks. 26,27 This method is valid only when the subfrequency ranges are known; changes in peak frequencies due to structural damage or environmental effects can lead to the selection of undesired peaks. A user-defined threshold value was additionally introduced to filter out less important peaks in each subfrequency range. [28][29][30][31] This approach ignores peaks that are lower than the user-defined threshold and thus reduces the number of false positives; however, the imposition of thresholds for the peak amplitude and subfrequency range critically limits the practical applications of the approach to different ...

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Stationarity‐Based Approach for the Selection of Lag Length in Cointegration Analysis Used for Structural Damage Detection

Cited by 20 publications

References 50 publications

Damage detection under temperature variability using closed-loop mode shapes

Damage detection under temperature variability using closed-loop mode shapes

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

Automated peak picking using region‐based convolutional neural network for operational modal analysis

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