Robust statistical damage localization with stochastic load vectors

Luciano Marin 1 Michael Döhler 2 Dionisio Bernal 3 Laurent Mevel 1
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
2 BAM Federal Institute for Materials Research and Testing
3 Northeastern University [Boston]
Abstract : 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.
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Article dans une revue
Structural Control and Health Monitoring, Wiley-Blackwell, 2015, 22 (3), pp.557-573. 〈10.1002/stc.1686〉
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Luciano Marin, Michael Döhler, Dionisio Bernal, Laurent Mevel. Robust statistical damage localization with stochastic load vectors. Structural Control and Health Monitoring, Wiley-Blackwell, 2015, 22 (3), pp.557-573. 〈10.1002/stc.1686〉. 〈hal-01063723〉

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