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Proceedings Paper

Damage identification through generalized correlations between measurements
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Paper Abstract

Several data-driven features have recently proven to be successful at detecting damage in structures. Some of these features, developed within the context of their state space attractors, highlight dynamics-specific changes without relying on model-specific forms or assumptions such as linearity. Features such as generalized interdependence and state space prediction error can also be formulated such that they provide information about generalized correlations between time series. Therefore, in addition to damage indications, these features can also provide details about the location of damage in a structure by comparing dynamical differences between measurements. This work proposes a framework for establishing such an analysis procedure that can detect presence, extent, location, and/or type of damage in a structure from a single feature. This approach is validated on a multi-degree of freedom oscillator.

Paper Details

Date Published: 11 April 2007
PDF: 12 pages
Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 65320Z (11 April 2007); doi: 10.1117/12.715845
Show Author Affiliations
L. A. Overbey, Univ. of California, San Diego (United States)
M. D. Todd, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 6532:
Health Monitoring of Structural and Biological Systems 2007
Tribikram Kundu, Editor(s)

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