Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations

Johann Priou; Szymon Greś; Alexander Mendler; Matthieu Perrault; Laurent Guerineau; Michael Döhler

doi:10.1007/978-3-031-61421-7_56

Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations

Johann Priou, Szymon Greś, Alexander Mendler, Matthieu Perrault, Laurent Guerineau, Michael Döhler^*

^*Corresponding author af dette arbejde

Institut for Byggeri og Bygningsdesign - Structural Dynamics and Geotechnical Engineering

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

1 Citationer (Scopus)

Abstract

The interpretation of stabilization diagrams and the tracking of modes over time are classical tasks in operational modal analysis. In this work, we present a method based on a greedy clustering that efficiently extracts the modal parameters from stabilization diagrams with covariance-driven subspace identification and integrated uncertainty quantification. Stability criteria are strongly based on the estimated modal parameter uncertainties. From the analysis of one, or several, stabilization diagrams, a set of modal parameters is defined, for it to be tracked over time in the next analysis step. The tracking is performed by an active search for the reference parameters in new datasets by combining stability criteria with efficient search heuristics. The resulting algorithm is efficient in tracking large parameter changes, which is demonstrated on the S101 Bridge benchmark under artificially introduced damages, as well as on data of the Munich Test Bridge, where the modal parameters are strongly affected by temperature variations.

Originalsprog	Engelsk
Titel	Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1
Redaktører	Carlo Rainieri, Carmelo Gentile, Manuel Aenlle López
Antal sider	8
Forlag	Springer Science and Business Media Deutschland GmbH
Publikationsdato	2024
Sider	581-588
ISBN (Trykt)	9783031614200
DOI	https://doi.org/10.1007/978-3-031-61421-7_56
Status	Udgivet - 2024
Begivenhed	10th International Operational Modal Analysis Conference, IOMAC 2024 - Naples, Italien Varighed: 22 maj 2024 → 24 maj 2024

Konference

Konference	10th International Operational Modal Analysis Conference, IOMAC 2024
Land/Område	Italien
By	Naples
Periode	22/05/2024 → 24/05/2024

Navn	Lecture Notes in Civil Engineering
Vol/bind	514 LNCE
ISSN	2366-2557

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Citationsformater

Priou, J., Greś, S., Mendler, A., Perrault, M., Guerineau, L., & Döhler, M. (2024). Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations. I C. Rainieri, C. Gentile, & M. Aenlle López (red.), Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1 (s. 581-588). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-61421-7_56

Priou, Johann ; Greś, Szymon ; Mendler, Alexander et al. / Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations. Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1. red. / Carlo Rainieri ; Carmelo Gentile ; Manuel Aenlle López. Springer Science and Business Media Deutschland GmbH, 2024. s. 581-588 ( Lecture Notes in Civil Engineering, Bind 514 LNCE).

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abstract = "The interpretation of stabilization diagrams and the tracking of modes over time are classical tasks in operational modal analysis. In this work, we present a method based on a greedy clustering that efficiently extracts the modal parameters from stabilization diagrams with covariance-driven subspace identification and integrated uncertainty quantification. Stability criteria are strongly based on the estimated modal parameter uncertainties. From the analysis of one, or several, stabilization diagrams, a set of modal parameters is defined, for it to be tracked over time in the next analysis step. The tracking is performed by an active search for the reference parameters in new datasets by combining stability criteria with efficient search heuristics. The resulting algorithm is efficient in tracking large parameter changes, which is demonstrated on the S101 Bridge benchmark under artificially introduced damages, as well as on data of the Munich Test Bridge, where the modal parameters are strongly affected by temperature variations.",

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author = "Johann Priou and Szymon Gre{\'s} and Alexander Mendler and Matthieu Perrault and Laurent Guerineau and Michael D{\"o}hler",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 10th International Operational Modal Analysis Conference, IOMAC 2024 ; Conference date: 22-05-2024 Through 24-05-2024",

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doi = "10.1007/978-3-031-61421-7\_56",

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Priou, J, Greś, S, Mendler, A, Perrault, M, Guerineau, L & Döhler, M 2024, Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations. i C Rainieri, C Gentile & M Aenlle López (red), Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1. Springer Science and Business Media Deutschland GmbH, Lecture Notes in Civil Engineering, bind 514 LNCE, s. 581-588, 10th International Operational Modal Analysis Conference, IOMAC 2024, Naples, Italien, 22/05/2024. https://doi.org/10.1007/978-3-031-61421-7_56

Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations. / Priou, Johann; Greś, Szymon; Mendler, Alexander et al.
Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1. red. / Carlo Rainieri; Carmelo Gentile; Manuel Aenlle López. Springer Science and Business Media Deutschland GmbH, 2024. s. 581-588 ( Lecture Notes in Civil Engineering, Bind 514 LNCE).

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

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AU - Priou, Johann

AU - Greś, Szymon

AU - Mendler, Alexander

AU - Perrault, Matthieu

AU - Guerineau, Laurent

AU - Döhler, Michael

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - The interpretation of stabilization diagrams and the tracking of modes over time are classical tasks in operational modal analysis. In this work, we present a method based on a greedy clustering that efficiently extracts the modal parameters from stabilization diagrams with covariance-driven subspace identification and integrated uncertainty quantification. Stability criteria are strongly based on the estimated modal parameter uncertainties. From the analysis of one, or several, stabilization diagrams, a set of modal parameters is defined, for it to be tracked over time in the next analysis step. The tracking is performed by an active search for the reference parameters in new datasets by combining stability criteria with efficient search heuristics. The resulting algorithm is efficient in tracking large parameter changes, which is demonstrated on the S101 Bridge benchmark under artificially introduced damages, as well as on data of the Munich Test Bridge, where the modal parameters are strongly affected by temperature variations.

AB - The interpretation of stabilization diagrams and the tracking of modes over time are classical tasks in operational modal analysis. In this work, we present a method based on a greedy clustering that efficiently extracts the modal parameters from stabilization diagrams with covariance-driven subspace identification and integrated uncertainty quantification. Stability criteria are strongly based on the estimated modal parameter uncertainties. From the analysis of one, or several, stabilization diagrams, a set of modal parameters is defined, for it to be tracked over time in the next analysis step. The tracking is performed by an active search for the reference parameters in new datasets by combining stability criteria with efficient search heuristics. The resulting algorithm is efficient in tracking large parameter changes, which is demonstrated on the S101 Bridge benchmark under artificially introduced damages, as well as on data of the Munich Test Bridge, where the modal parameters are strongly affected by temperature variations.

KW - automated interpretation

KW - greedy clustering

KW - Operational modal analysis

KW - stabilization diagram

KW - subspace methods

KW - uncertainty quantification

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A2 - Aenlle López, Manuel

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Priou J, Greś S, Mendler A, Perrault M, Guerineau L, Döhler M. Automated Uncertainty-Based Clustering and Tracking of Modal Parameters Under Strong Variations. I Rainieri C, Gentile C, Aenlle López M, red., Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1. Springer Science and Business Media Deutschland GmbH. 2024. s. 581-588. ( Lecture Notes in Civil Engineering, Bind 514 LNCE). doi: 10.1007/978-3-031-61421-7_56