SHM Based System Design of a Wind Turbine Tower Using a Modal Sensitivity Based Bayes Detector

Abstract : It is investigated if material based structural safety can be replaced with safety obtained from SHM. SHM on its own does not add any value to a structure unless there is a decision policy attached. The further attachment of a loss function enables upfront calculation of the expected utility. In the presented case, damage sensitive features from sensitivity based damage detection are used in a supervised learning and optimization scheme. A Bayes detector, i.e. the static decision rule that minimizes the expected utility, is utilized for the system design. The technique is demonstrated in a simulation case of the NREL 5MW wind turbine tower subjected to bending fatigue and horizontal circumferential cracking at weld locations. Decision driven SHM is shown to change the initial design safety of the structure in the fatigue limit state. A common optimum of material safety and classifier threshold is found, enabling the calculation of the expected value of SHM. A sensitivity analysis of the feature extraction and of the loss function is included.
Type de document :
Communication dans un congrès
Le Cam, Vincent and Mevel, Laurent and Schoefs, Franck. EWSHM - 7th European Workshop on Structural Health Monitoring, Jul 2014, Nantes, France. 2014
Liste complète des métadonnées

Littérature citée [12 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01020349
Contributeur : Anne Jaigu <>
Soumis le : mardi 8 juillet 2014 - 09:58:02
Dernière modification le : mardi 9 septembre 2014 - 09:51:21
Document(s) archivé(s) le : mercredi 8 octobre 2014 - 11:35:19

Fichier

0186.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01020349, version 1

Collections

Citation

Mads Knude Hovgaard, Rune Brincker. SHM Based System Design of a Wind Turbine Tower Using a Modal Sensitivity Based Bayes Detector. Le Cam, Vincent and Mevel, Laurent and Schoefs, Franck. EWSHM - 7th European Workshop on Structural Health Monitoring, Jul 2014, Nantes, France. 2014. 〈hal-01020349〉

Partager

Métriques

Consultations de la notice

180

Téléchargements de fichiers

247