Aarhus University Seal

Uncertainty quantification of input matrices and transfer function in input/output subspace system identification

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Szymon Gres
  • Michael Döhler, INRIA, Campus de Beaulieu, F-35042 Rennes, France
  • Niels-Jørgen Jacobsen, Brüel & Kjær Sound & Vibration Measurement A/S, Denmark
  • Laurent Mevel, INRIA, Campus de Beaulieu, F-35042 Rennes, France

The transfer function of a linear mechanical system can be defined in terms of the quadruplet of state-space matrices (A,B,C,D) that can be identified from input and output measurements with subspace-based system identification methods. The estimation of the quadruplet has been well studied in the literature from both theoretical and practical viewpoints. Nonetheless, a practical algorithm for uncertainty quantification of its estimation errors and the uncertainty of the resultant parametric transfer function is missing in the context of subspace identification. For several output-only and input/output subspace methods, the covariance related to the matrices (A,C) and to the resulting modal parameters can be effectively obtained with recently developed first-order perturbation-based schemes, while the corresponding uncertainty quantification for the input-related matrices (B,D) is missing. In this paper, explicit expressions for the covariance related to matrices (B,D) are developed, and applied to the covariance estimation of the resulting transfer function. The proposed schemes are validated on a simulated data of a mechanical system and are applied to laboratory measurements of a plate.

Original languageEnglish
Article number108581
JournalMechanical Systems and Signal Processing
IssuePart B
Number of pages17
Publication statusPublished - 15 Mar 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

    Research areas

  • Input/output data, Linear time-invariant systems, Subspace methods, Transfer function, Variance computation, DAMAGE LOCALIZATION, ALGORITHMS, SPACE, CONSISTENCY, Input, WIND TURBINE, output data, VARIABLES, COMPUTATION, VARIANCE

See relations at Aarhus University Citationformats

ID: 226356877