Automated operational modal analysis using variational Gaussian mixture model. (15th December 2022) - Zeng, Jice
Highlights: Identification of modal parameters automatically. Variational Gaussian mixture model for soft classification. Using only two user-specified thresholds. Automatically determine the optimal number of clusters. Abstract: Automated operational modal analysis is essential for online structural health monitoring without human intervention. It remains a challenging issue due to the need of processing a large number of datasets and the involvement of many user-specified thresholds. This paper proposes a novel automated modal identification approach based on stochastic subspace identification and variational Gaussian mixture model that involves the analysis of the stabilization diagram to automatically identify modal parameters. Two validation criteria are first adopted to eliminate the spurious modes in the stabilization diagram. A Gaussian mixture model (GMM) is then used to probabilistically classify each pole in the stabilization diagram to a specific cluster. The parameters of GMM are estimated using variational inference, giving representatives of each mode, and the optimal number of clusters is automatically determined through the Dirichlet Process. The proposed framework automatically distinguishes physical modes from spurious modes with only two verified and widely used thresholds. Results of a four-story shear and a footbridge with continuous measurements demonstrate the efficacy and robustness of the proposed approach. It shows that the proposed approach can automatically identify modal parameters with high accuracy, including weakly excited and closely spaced modes.