Subspace-based damage detection with rejection of the temperature effect and uncertainty in the reference
| Autor: | Viefhues, E., Michael Döhler, Zhang, Q., Hille, F., Mevel, L. |
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| Přispěvatelé: | BAM Federal Institute for Materials Research and Testing, Federal Institute for Materials Research and Testing - Bundesanstalt für Materialforschung und -prüfung (BAM), 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-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Döhler, Michael |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IOMAC 2019-8th International Operational Modal Analysis Conference IOMAC 2019-8th International Operational Modal Analysis Conference, May 2019, Copenhagen, Denmark. pp.1-11 Scopus-Elsevier |
| Popis: | International audience; Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches-building a reference state from an averaging method or a piecewise method-a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches. |
| Databáze: | OpenAIRE |
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