Return to search

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.

Identiferoai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00904087
Date02 October 2013
CreatorsGallegos Marin, Luciano Heitor
PublisherUniversité Rennes 1
Source SetsCCSD theses-EN-ligne, France
LanguageEnglish
Detected LanguageEnglish
TypePhD thesis

Page generated in 0.0022 seconds