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.…
Autor*innen: | Eva Viefhues, M. Döhler, Falk HilleORCiD, L. Mevel |
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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 |
Erste Seite: | 1007 |
Letzte Seite: | 1014 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau |
Freie Schlagwörter: | Damage detection; Subspace-methods; Uncertainty |
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 |