A new perspective on Bayesian Operational Modal Analysis
<|reference_start|>A new perspective on Bayesian Operational Modal Analysis: In the field of operational modal analysis (OMA), obtained modal information is frequently used to assess the current state of aerospace, mechanical, offshore and civil structures. However, the stochasticity of operational systems and the lack of forcing information can lead to inconsistent results. Quantifying the uncertainty of the recovered modal parameters through OMA is therefore of significant value. In this article, a new perspective on Bayesian OMA is proposed: a Bayesian stochastic subspace identification (SSI) algorithm. Distinct from existing approaches to Bayesian OMA, a hierarchical probabilistic model is embedded at the core of covariance-driven SSI. Through substitution of canonical correlation analysis with a Bayesian equivalent, posterior distributions over the modal properties are obtained. Two inference schemes are presented for the proposed Bayesian formulation: Markov Chain Monte Carlo and variational Bayes. Two case studies are then explored. The first is benchmark study using data from a simulated, multi degree-of-freedom, linear system. Following application of Bayesian SSI, it is shown that the same posterior is targeted and recovered by both inference schemes, with good agreement between the posterior mean and the conventional SSI result. The second study applies the variational form to data obtained from an in-service structure: The Z24 bridge. The results of this study are presented at single model orders, and then using a stabilisation diagram. The recovered posterior uncertainty is presented and compared to the classic SSI result. It is observed that the posterior distributions with mean values coinciding with the natural frequencies exhibit lower variance than values situated away from the natural frequencies.<|reference_end|>
@article{o'connell2024a,
    title={A new perspective on Bayesian Operational Modal Analysis},
    author={Brandon J. O'Connell, Max D. Champneys, Timothy J. Rogers},
    journal={arXiv preprint arXiv:2408.08664},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08664},
    primaryClass={stat.ML cs.LG cs.SY eess.SY}
}