Statistical decision making for damage localization with stochastic load vectors

Döhler, Michael; Marin, Regina Paiva Melo; Bernal, Dionisio; Mevel, Laurent

doi:10.1016/j.ymssp.2012.12.011

Cited by 29 publications

(57 citation statements)

References 19 publications

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“…While modal truncation and variance errors of the required modal parameters may perturb results, a consistent statistical analysis can at least in part compensate and improve the damage localization resolution. Further details of are found in [13,14].…”

Section: Discussionmentioning

confidence: 99%

“…In [13,14] the uncertainty propagation from system identification results to the computed stress vectors S(s i ) at the selected the Laplace variables s i was analyzed. In this section, these results are summarized and the respective statistical test is shown.…”

Section: Statistical Damage Localizationmentioning

confidence: 99%

“…Then, these covariances are propagated to the stress vector as follows. Note that a more detailed description of this step is available in [13,14]. In a first step, the sensitivity of R(s) in (3) with respect to the system matrices A c and C c is obtained, yielding…”

Section: Covariance Computation Of Stress Vectormentioning

confidence: 99%

“…Let the respective stress vector and (sub-)covariance matrix be denoted as S j and j . Since an estimate of vector S j is asymptotically Gaussian distributed [13], it can finally be tested for being 0 and thus potentially damaged with the χ 2 -test variable…”

Section: Statistical Tests For Each Elementmentioning

confidence: 99%

“…Taking into account these uncertainties proves to be important for structural damage diagnosis. Based on these considerations, a statistical extension of the SDDLV method was developed in [13] for the actual decision if an element is damaged. In [14] a more robust version has been developed, where a joint statistical evaluation of the stress computation is carried out at several Laplace domain variables s where the transfer function matrix difference is evaluated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Operational modal analysis with uncertainty quantification for SDDLV-based damage localization

Döhler

Marin²,

Mevel³

et al. 2015

MATEC Web of Conferences

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Abstract. The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on a finite element model of a structure in a reference state and output-only measurements in both reference and damaged states. A stress field is computed for loads in the null space of a surrogate of the change in the transfer matrix at the sensor positions, where the null space is obtained based on the identified modal parameters in both structural states. Then, the damage location is related to positions where the stress is close to zero. The localization results of this generic approach are perturbed by mainly two sources: modal truncation (not all modes of the structure are available) and modal parameter identification errors (estimation is subject to statistical uncertainties). In this paper, we show how damage localization with the SDDLV approach is improved by taking into account the estimation uncertainties of the underlying identified modal parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Damage Localizationmentioning

confidence: 99%

Section: Covariance Computation Of Stress Vectormentioning

confidence: 99%

Section: Statistical Tests For Each Elementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational modal analysis with uncertainty quantification for SDDLV-based damage localization

Döhler

Marin²,

Mevel³

et al. 2015

MATEC Web of Conferences

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Robust statistical damage localization with stochastic load vectors

Marin

Döhler

Bernal

et al. 2014

Struct. Control Health Monit.

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Summary The stochastic dynamic damage locating vector approach is a vibration‐based damage localization method based on a finite element model of a structure and output‐only measurements in both reference and damaged states. A stress field is computed for loads in the null space of a surrogate of the change in the transfer matrix at the sensor positions for some values in the Laplace domain. Then, the damage location is related to positions where the stress is close to zero. Robustness of the localization information can be achieved by aggregating results at different values in the Laplace domain. So far, this approach, and in particular the aggregation, is deterministic and does not take the uncertainty in the stress estimates into account. In this paper, the damage localization method is extended with a statistical framework. The uncertainty in the output‐only measurements is propagated to the stress estimates at different values of the Laplace variable, and these estimates are aggregated based on statistical principles. The performance of the new statistical approach is demonstrated both in a numerical application and a lab experiment, showing a significant improvement of the robustness of the method due to the statistical evaluation of the localization information. Copyright © 2014 John Wiley & Sons, Ltd.

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Rank-revealing QR decomposition applied to damage localization in truss structures

Błachowski

Zhong

et al. 2016

Struct. Control Health Monit.

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The purpose of this work is the development of an efficient and high-sensitive damage localization technique for truss structures, based on the rank-revealing QR decomposition (RRQR) of the difference-of-flexibility matrix. The method is an enhancement of the existing techniques of damage detection, which rely on the set of socalled damage locating vector (DLV). The advantages of the RRQR decomposition-based DLV (RRQR-DLV) method are its less computational effort and high sensitivity to damage. Compared with the frequently used stochastic DLV (SDLV) method, RRQR-DLV offers higher sensitivity to damage, which has been validated based on the presented numerical simulation. The effectiveness of the proposed RRQR-DLV method is also illustrated with the experimental validation based on a laboratory-scale Bailey truss bridge model. The proposed method works under ambient excitation such as traffic excitation and wind excitation; therefore, it is promising for real-time damage monitoring of truss structures.

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Statistical decision making for damage localization with stochastic load vectors

Cited by 29 publications

References 19 publications

Operational modal analysis with uncertainty quantification for SDDLV-based damage localization

Operational modal analysis with uncertainty quantification for SDDLV-based damage localization

Robust statistical damage localization with stochastic load vectors

Rank-revealing QR decomposition applied to damage localization in truss structures

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