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Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Yang, Yongchao and Dorn, Charles (2020) Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study. Journal of Sound and Vibration, 483 . Art. No. 115473. ISSN 0022-460X. https://resolver.caltech.edu/CaltechAUTHORS:20200601-085237848

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Abstract

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. Although existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the “message-passing” procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jsv.2020.115473DOIArticle
ORCID:
AuthorORCID
Yang, Yongchao0000-0003-1776-3306
Dorn, Charles0000-0001-6516-2586
Additional Information:© 2020 Published by Elsevier Ltd. Received 10 January 2020, Revised 26 April 2020, Accepted 21 May 2020, Available online 30 May 2020.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Subject Keywords:Structural dynamics; Full-field measurement; Operational modal analysis; Clustering; Visualization; Video processing
Record Number:CaltechAUTHORS:20200601-085237848
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200601-085237848
Official Citation:Yongchao Yang, Charles Dorn, Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study, Journal of Sound and Vibration, Volume 483, 2020, 115473, ISSN 0022-460X, https://doi.org/10.1016/j.jsv.2020.115473. (http://www.sciencedirect.com/science/article/pii/S0022460X20303059)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103579
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Jun 2020 15:59
Last Modified:26 Jun 2020 16:35

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