Bayesian FFT Modal Identification for Multi-setup Experimental Modal Analysis

TL;DR

This paper introduces a Bayesian modal identification method for multi-setup experimental modal analysis, enabling high-resolution mode shape capture with limited sensors, validated through synthetic and field data.

Contribution

It extends a single-setup Bayesian EMA algorithm to multi-setup scenarios, providing a probabilistic framework with efficient computation for structural modal analysis.

Findings

High consistency with adequate test setups

Accurate modal parameter estimation with limited sensors

Enhanced computational efficiency through analytical expressions

Abstract

In full-scale forced vibration tests, the demand often arises to capture high-spatial-resolution mode shapes with limited number of sensors and shakers. Multi-setup experimental modal analysis (EMA) addresses this challenge by roving sensors and shakers across multiple setups. To enable fast and accurate multi-setup EMA, this paper develops a Bayesian modal identification strategy by extending an existing single-setup algorithm. Specifically, a frequency-domain probabilistic model is first formulated using multiple sets of structural multiple-input, multiple-output (MIMO) vibration data. A constrained Laplace method is then employed for Bayesian posterior approximation, providing the maximum a posteriori estimates of modal parameters along with a posterior covariance matrix (PCM) for uncertainty quantification. Utilizing complex matrix calculus, analytical expressions are derived for parameter updates in the coordinate descent optimization, as well as for PCM computation, enhancing both coding simplicity and computational efficiency. The proposed algorithm is intensively validated by investigating empirical examples with synthetic and field data. It demonstrates that the proposed method yields highly consistent results compared to scenarios with adequate test equipment. The resulting high-fidelity MIMO model enables structural response prediction under future loading conditions and supports condition assessment.