GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions
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 ex...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Journal of Sensor and Actuator Networks |
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| Online Access: | https://www.mdpi.com/2224-2708/9/3/41 |
| _version_ | 1797554248248983552 |
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| author | Konstantinos Tatsis Vasilis Dertimanis Yaowen Ou Eleni Chatzi |
| author_facet | Konstantinos Tatsis Vasilis Dertimanis Yaowen Ou Eleni Chatzi |
| author_sort | Konstantinos Tatsis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:28:17Z |
| format | Article |
| id | doaj.art-102131e20a1b4acb89ae6eff1f3ddb93 |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-10T16:28:17Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-102131e20a1b4acb89ae6eff1f3ddb932023-11-20T12:59:21ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082020-09-01934110.3390/jsan9030041GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental ConditionsKonstantinos Tatsis0Vasilis Dertimanis1Yaowen Ou2Eleni Chatzi3Chair of Structural Mechanics & Monitoring, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, SwitzerlandChair of Structural Mechanics & Monitoring, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, SwitzerlandChair of Structural Mechanics & Monitoring, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, SwitzerlandChair of Structural Mechanics & Monitoring, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, SwitzerlandThe 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.https://www.mdpi.com/2224-2708/9/3/41structural health monitoringvarying environmental and operational conditionsdamage detection and localizationGaussian process regressionautoregressive with exogenous inputsdistributed sensor network |
| spellingShingle | Konstantinos Tatsis Vasilis Dertimanis Yaowen Ou Eleni Chatzi GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions Journal of Sensor and Actuator Networks structural health monitoring varying environmental and operational conditions damage detection and localization Gaussian process regression autoregressive with exogenous inputs distributed sensor network |
| title | GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions |
| title_full | GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions |
| title_fullStr | GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions |
| title_full_unstemmed | GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions |
| title_short | GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions |
| title_sort | gp arx based structural damage detection and localization under varying environmental conditions |
| topic | structural health monitoring varying environmental and operational conditions damage detection and localization Gaussian process regression autoregressive with exogenous inputs distributed sensor network |
| url | https://www.mdpi.com/2224-2708/9/3/41 |
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