Dispersion–Corrected, Operationally Normalized Stabilization Diagrams for Robust Structural Identification

Dertimanis, Vasilis K.; Spiridonakos, Minas D.; Chatzi, Eleni

doi:10.1007/978-3-319-15224-0_8

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…In establishing Ψ, attention should be paid to the proper distinction of structural from "erroneous" modes, as well as to the matching of eigenvectors associated to the same mode. This is accomplished by applying dispersion analysis [70][71][72] and formulating the modal assurance criterion (MAC).…”

Section: The Damage Detection and Localization Phasementioning

confidence: 99%

“…The BIC and the MSE both decay slowly and stabilize at high orders (p > 30). To elaborate further on the model order selection, Figure 8c shows the frequency stabilization diagrams (FSDs), corrected using the dispersion analysis method described in Dertimanis and Chatzi [71]. The correction is based on the energy associated to each estimated structural vibration mode and on a low threshold (herein set as greater than 1% of the mode with the highest energy content) that distinguishes structural from erroneous, or "artificial" modes [70].…”

Section: Damage Detection On a Spring-mass-damper Systemmentioning

confidence: 99%

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GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions

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Dertimanis

et al. 2020

JSAN

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The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

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Section: The Damage Detection and Localization Phasementioning

confidence: 99%

Section: Damage Detection On a Spring-mass-damper Systemmentioning

confidence: 99%