Statistical damage localization with stochastic load vectors using multiple mode sets

Abstract : The Stochastic Dynamic Damage Locating Vector (SDDLV) method is an output-only damage localization method based on both a Finite Element (FE) model of the structure and modal parameters estimated from output-only measurements in the damage and reference states of the system. A vector is obtained in the null space of the changes in the transfer matrix from both states and then applied as a load vector to the model. The damage location is related to this stress where it is close to zero. In previous works, an important theoretical limitation was that the number of modes used in the computation related to the transfer function could not be higher than the number of sensors located on the structure. It would be nonetheless desirable not to discard information from the identification procedure. In this paper, the SDDLV method has been extended with a joint statistical approach for multiple mode sets, overcoming this restriction on the number of modes. Another problem is that the performance of the method can change considerably depending of the Laplace variable where the transfer function is evaluated. Particular attention is given to this choice and how to optimize it. The new approach is validated in numerical simulations and on experimental data, where the outcomes for multiple mode sets are compared with only using a single mode set. From these results, it can be seen that the success rate of finding the correct damage localization is increased when using multiple mode sets instead of a single mode set.
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Structural Health Monitoring, SAGE Publications (UK and US), 2017, 16 (5), pp.518-535. 〈10.1177/1475921717714447〉
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Soumis le : vendredi 25 août 2017 - 10:51:59
Dernière modification le : mercredi 11 avril 2018 - 01:51:15

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Md Delwar Hossain Bhuyan, Michael Döhler, Yann Lecieux, Laurent Mevel, Franck Schoefs. Statistical damage localization with stochastic load vectors using multiple mode sets. Structural Health Monitoring, SAGE Publications (UK and US), 2017, 16 (5), pp.518-535. 〈10.1177/1475921717714447〉. 〈hal-01577250〉

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