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Stochastic subspace-based damage detection with uncertainty in the reference null space

  • This paper deals with uncertainty considerations in damage diagnosis using the stochastic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases theThis paper deals with uncertainty considerations in damage diagnosis using the stochastic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases the probability of detection when the reference model estimate is subject to high uncertainty, leading to a more reliable test.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Eva Viefhues, M. Döhler, Falk Hille, L. Mevel
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Proceedings of the 11th International Workshop on Structural Health Monitoring 2017: Real-Time State Awareness and Data-Driven Safety Assurance
Jahr der Erstveröffentlichung:2017
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
Erste Seite:1007
Letzte Seite:1014
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Damage detection; Subspace-methods; Uncertainty
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Sicherheit und Lebensdauer von Bauwerken
Veranstaltung:International Workshop on Structural Health Monitoring
Veranstaltungsort:Stanford, CA, USA
Beginndatum der Veranstaltung:12.09.2017
Enddatum der Veranstaltung:14.09.2017
ISBN:978-1-60595-330-4
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:07.11.2017
Referierte Publikation:Nein