Paper
11 April 2007 Damage identification through generalized correlations between measurements
Author Affiliations +
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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
L. A. Overbey and M. D. Todd "Damage identification through generalized correlations between measurements", Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 65320Z (11 April 2007); https://doi.org/10.1117/12.715845
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Cited by 1 scholarly publication.
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KEYWORDS
Error analysis

Signal to noise ratio

Structural health monitoring

Analytical research

Statistical analysis

Damage detection

Feature extraction

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