Operational modal analysis with automated SSI-COV algorithm

Version 2.5 (2.37 MB) by E. Cheynet
The modal parameters of a line-like structure are automatically identified using an SSI-COV algorithm applied to ambient vibration data
2.9K Downloads
Updated 4 Feb 2021

Operational modal analysis with automated SSI-COV algorithm

The modal parameters of a line-like structure are automatically identified using an SSI-COV algorithm applied to ambient vibration data

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Summary

The function SSICOV.m aims to automatically identify the eigenfrequencies, mode shapes and damping ratios of a line-like structure using ambient vibrations only. The covariance-driven stochastic subspace identification method (SSI-COV) is used in combination with a clustering algorithm to automatically analyse the stabilization diagrams.

The algorithm is inspired by the one used by Magalhaes et al. [1]. It has been applied for ambient vibration monitoring of the Lysefjord Bridge [2] and was compared to the frequency domain decomposition technique [3]. Finally, the algorithm was found accurate enough to visualise the evolution of the bridge eigenfrequencies with the temperature [4].

content

The submission file contains:

  • A data file BridgeData.mat
  • A Matlab Live Script Example1.mlx that illustrates the application of the algorithm.
  • A Matlab Live Script Example1_noToolbox.mlx that reproduce Example1 but using the function SSICOV_noToolbox.
  • The function SSICOV which is the automated SSI-COV algorithm.
  • The function SSICOV_noToolbox which is the automated SSI-COV algorithm but does not use the Statistics and Machine Learning Toolbox. The Linkage algorithm is replaced by the function "PHA_Clustering" by [5] and the function "cluster" is replaced by the function "Cluster2", which is derived from [6].
  • The function plotStabDiag.m, which plot the stabilization diagram.

Any question, suggestion or comment is welcomed.

References

[1] Magalhaes, F., Cunha, A., & Caetano, E. (2009). Online automatic identification of the modal parameters of a long span arch bridge. Mechanical Systems and Signal Processing, 23(2), 316-329.

[2] Cheynet, E., Jakobsen, J. B., & Snæbjörnsson, J. (2016).Buffeting response of a suspension bridge in complex terrain. Engineering Structures, 128, 474-487.

[3] Cheynet, E., Jakobsen, J. B., & Snæbjörnsson, J. (2017).Damping estimation of large wind-sensitive structures.Procedia Engineering, 199, 2047-2053.

[4] Cheynet, E., Snæbjörnsson, J., & Jakobsen, J. B. (2017).Temperature Effects on the Modal Properties of a Suspension Bridge.In Dynamics of Civil Structures, Volume 2 (pp. 87-93). Springer.

[5] Yonggang (2021). Fast hierarchical clustering method - PHA (https://www.mathworks.com/matlabcentral/fileexchange/46134-fast-hierarchical-clustering-method-pha), MATLAB Central File Exchange. Retrieved February 4, 2021.

[6] Eric Ogier (2021). Hierarchical clustering (https://www.mathworks.com/matlabcentral/fileexchange/56844-hierarchical-clustering), MATLAB Central File Exchange. Retrieved February 4, 2021.

Cite As

Cheynet, E. Operational Modal Analysis with Automated SSI-COV Algorithm. Zenodo, 2020, doi:10.5281/ZENODO.3774061.

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MATLAB Release Compatibility
Created with R2018b
Compatible with R2014b to R2019b
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
2.5

See release notes for this release on GitHub: https://github.com/ECheynet/SSICOV/releases/tag/2.5

2.4

See release notes for this release on GitHub: https://github.com/ECheynet/SSICOV/releases/tag/v2.4

2.3

See release notes for this release on GitHub: https://github.com/ECheynet/SSICOV/releases/tag/v2.3

2.2

Added Github repository

2.1

typos corrected + minor modification to improve the performances

2.0.2

typo

2.0.1

Set the default value of Ts as 500*dt instead of 20 s to avoid the crashing problem of Matlab if a very high sampling frequency is used. Ideally, Ts should be between two and six times the value of the lowest eigenfrequency of the system.

2.0.0

The distance parameter "pos" for the clustering algorithm is now properly defined using both the difference in terms of frequencies and the MAC number (thank Mihhail Samusev for the help!). Finally, the description of the submission is updated.

1.0.4

Updated the definition for the variable "pos" (Cluster analysis) + The default value for the time lag (cross-covariance) is more robustly defined + the variable T1 is preallocated

1.0.3

Description

1.0.2

Updated description

1.0.1

typo

1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.