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Conference Papers Year : 2022

Statistical damage detection and localization with Mahalanobis distance applied to modal parameters

Abstract

Damage detection and damage localization constitute two pillars of Structural Health Monitoring that are highly relevant for applications to large-scale structures. Damage detection is usually achieved through statistical tests of data-driven residuals that monitor changes of a structure from its baseline behaviour. Damage localization investigates changes in damage residuals with respect to parameterized structural models through sensitivity vectors. Among the classic damage-sensitive features used for residual generation are subspace angles and principal components obtained from data spaces, whose evaluation for a decision about damage often boils down to novelty analysis, or statistical likelihood ratio tests. Modal parameter estimates are also employed for this purpose; however, most of the existing approaches appear to neglect the uncertainties related to their estimation. This paper fills this gap and presents a residual for damage detection and damage localization that is based on the difference of modal parameters obtained from data collected in some baseline and some test state of the structural system. The proposed scheme is evaluated in numerical simulations validating its robustness for damage detection and damage localization.
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Dates and versions

hal-03722669 , version 1 (13-07-2022)

Identifiers

  • HAL Id : hal-03722669 , version 1

Cite

Szymon Gres, Alexander Mendler, Niels-Jørgen Jacobsen, Palle Andersen, Michael Döhler. Statistical damage detection and localization with Mahalanobis distance applied to modal parameters. IOMAC 2022 - 9th International Operational Modal Analysis Conference, Jul 2022, Vancouver, Canada. pp.1-8. ⟨hal-03722669⟩
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