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.

翻译：在全尺度强迫振动试验中，常需利用有限数量的传感器与激振器获取高空间分辨率的振型。多测点试验模态分析通过在不同测点间移动传感器与激振器来解决这一难题。为实现快速、准确的多测点试验模态分析，本文通过扩展现有单测点算法，提出一种贝叶斯模态识别策略。具体而言，首先利用多组结构多输入多输出振动数据建立频域概率模型；随后采用约束拉普拉斯方法进行贝叶斯后验近似，提供模态参数的最大后验估计及用于不确定性量化的后验协方差矩阵。通过应用复矩阵微积分，推导出坐标下降优化中参数更新及后验协方差矩阵计算的解析表达式，从而提升编码简洁性与计算效率。所提算法通过合成数据与现场数据的实证案例进行了深入验证。结果表明，与配备充足测试设备的情况相比，本方法能获得高度一致的结果。所得高保真多输入多输出模型可用于未来荷载条件下的结构响应预测，并为状态评估提供支持。