Statistical decision making for stochastic damage localization approaches

Mechanical systems under vibration excitation are prime candidate for being modeled by linear time invariant systems. Damage localization using both finite element information and modal parameters estimated from ambient vibration data collected from sensors is possible by the Stochastic Dynamic Damage Location Vector (SDDLV) approach, where the damage location is empirically related to positions where the stress is close to zero. The first contribution in this thesis shows how the uncertainty in the estimates of the state space system can be used to derive uncertainty bounds on the damage localization residuals to decide about the damage location with a hypothesis test using one chosen Laplace value. In the second contribution, the damage localization method is extended with a statistical framework and robustness of the localization information is achieved by aggregating results at different values in the Laplace domain. The Influence Line Damage Location (ILDL) is a complementary approach of the SDDLV where the subspace angle is computed and damage is empirically located at points near zero. The last contribution describes how robustness of the localization information is achieved by aggregating results at different values in the Laplace domain based on the previous two contributions. The proposed methods are validated and successfully applied to damage localization of several applications in civil structures.

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Source https://theses.hal.science/tel-00904087
Author Marin, Luciano Heitor Gallegos
Maintainer CCSD
Last Updated May 8, 2026, 06:18 (UTC)
Created May 8, 2026, 06:18 (UTC)
Identifier NNT: 2013REN1S059
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Statistical Inference for Structural Health Monitoring (I4S) ; Département Composants et Systèmes (IFSTTAR/COSYS) ; Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France-Centre Inria de l'Université de Rennes ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
creator Marin, Luciano Heitor Gallegos
date 2013-10-02T00:00:00
harvest_object_id 7480364a-a5e1-492d-8e6f-93b246bb6e5f
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE