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Cited by 12

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

Tatsis, Konstantinos, Dertimanis, Vasilis, Ou, Yaowen, Chatzi, Eleni·2020·Journal of Sensor and Actuator Networks
DOI 10.3390/jsan9030041
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Abstract

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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Cited by

Metadata

Authors
Tatsis, K., Dertimanis, V., Ou, Y., & Chatzi, E.
Affiliations
Chair of Structural Mechanics & Monitoring, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland
ORCID
Venue
Journal of Sensor and Actuator Networks
Publisher
MDPI AG
Published
2020-09-08
Volume
9
Issue
3
Pages
41
ISSN
2224-2708
DOI
10.3390/jsan9030041
Cited by
12
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
European Research Council (ERC-2015-StG #679843 and ERC-2018-PoCWINDMIL RT-DT)

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