A fast collapsed Gibbs sampler for frequency domain operational modal analysis. (1st July 2022)

• Record Type: Journal Article
• Authors: Dollon, Quentin; Antoni, Jérôme; Tahan, Antoine; Gagnon, Martin; Monette, Christine
• Is Part Of: Mechanical systems and signal processing. Volume 173(2022)
• Publication Date: 2022-07-01
• Subjects: Operational modal analysis -- Bayesian inference -- Gibbs sampling -- Uncertainty quantification -- FFT approach; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108985
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper introduces a fast Gibbs sampler for solving a fully Bayesian problem in operational modal analysis. The proposed method is able to infer modal properties from the FFT of well-separated modes. System identification and related uncertainties are captured by a posterior distribution. The classical resonance description is wrapped into a hierarchical probabilistic model. The model is sampled through an enhanced Gibbs sampler including a Metropolis–Hasting step. The entire sampling scheme is new and the fast convergence of the algorithm is enabled by two strategies. First, the use of a collapsed Gibbs algorithm allows for an efficient sampling of the mode shape. Second, the use of adequate candidate distributions in the Metropolis–Hasting step provides excellent acceptance ratios, around 75%. Eventually, the numerical inference procedure is compared to the state-of-the-art Fast Bayesian FFT Algorithm (Fast-BFFTA), which is the most commonly used algorithm for Bayesian operational modal analysis. The sampler surpasses the Fast-BFFTA for small data-based identification, while remaining sustainable in terms of computing requirements. Highlights: Enhanced Gibbs sampler to achieve fully-Bayesian operational modal analysis. Collapsed algorithm enables a fast convergence. Metropolis–Hasting candidate distributions provide high acceptance ratios. Heteroscedastic modelling of the error. Reliable results regardless of the data size.