Vision-Based Structural Damage Identification in Vibrating Beams via Dynamic Mode Decomposition
For structural health monitoring, this work offers a non-contact, interpretable damage detection method using video data, but it is an incremental application of existing DMD techniques to a specific problem.
This paper presents a Dynamic Mode Decomposition (DMD) framework for structural damage identification from high-speed video of vibrating beams. The method extracts modal frequencies and mode shapes, and a damage index based on these features successfully distinguishes healthy and damaged states in both simulations and experiments.
Structural damage detection using non-contact sensing remains a challenging problem in structural health monitoring. This study presents a data-driven framework based on Dynamic Mode Decomposition (DMD) for extracting structural dynamics directly from high-speed video recordings of vibrating structures. Within this approach, the underlying dynamics are approximated by a linear operator, whose spectral decomposition yields modal frequencies and corresponding spatial mode shapes, enabling a physically interpretable representation of the system response. The proposed methodology is evaluated through both numerical and experimental investigations. First, a cantilever beam model is simulated in ANSYS under healthy and damaged conditions. DMD is applied to partial observation data to reconstruct and predict the system response, while the extracted modal features are analyzed to characterize damage-induced variations. Second, high-speed video recordings of the beam are processed into spatiotemporal snapshot matrices, allowing DMD to recover full-field dynamic behavior without contact sensors. To enable quantitative assessment, a damage index is formulated based on DMD-derived modal features, capturing deviations in both frequency content and spatial characteristics. The results demonstrate consistent and distinguishable patterns between healthy and damaged states across both simulation and experiments, highlighting the capability of DMD as a robust and interpretable tool for non-contact damage detection using video data.