Framework for event-based modal parameter estimation

Abstract

For structural dynamics and health monitoring, vision-based vibration measurement has become an attractive approach thanks to its full-field, non-contact, and cost-effective aspect. However, conventional frame-based cameras are inherently constrained by frame rate, motion blur, and exposure duration, while producing vast amounts of redundant data. These limitations restrict their ability to capture high-frequency vibrations over extended periods or under low illumination. In contrast, event cameras asynchronously record pixel-level brightness changes with microsecond latency and a high dynamic range. This enables high-frequency motion tracking under ambient light or uneven light condition and ensures efficient data use, as events are generated only where motion occurs. This study presents a unified event-based framework for estimating vibration and modal parameters within a dynamic state-space formulation. Displacement information is extracted from images reconstructed from event streams, while velocity information is obtained directly from the events. By combining these complementary motion signals with a physics-based vibration model via data assimilation, the method enables accurate and continuous estimation of structural vibrations with strong robustness to noise. Experiments were performed on a cantilever beam with an event camera and a reference accelerometer synchronized in operation, including both free and forced vibration tests. In the free-vibration tests, modal parameters were identified from the decaying response, whereas the forced-vibration setup used a shaker to apply harmonic excitation, enabling the estimation of the operational deflection shapes under steady-state conditions. The extracted modal properties closely matched the accelerometer measurements, validating the proposed approach. The proposed framework demonstrates the capability of the event-camera-based approach to measure structural vibrations with high temporal fidelity and strong robustness to noise. It combines event data with a physics-based vibration model through data assimilation. This integration enables accurate modal identification and efficient motion estimation without the motion blur, saturation, or illumination constraints inherent to frame-based systems. These findings highlight the potential of event cameras as a robust and data-efficient sensing modality for continuous, high-frequency monitoring in structural dynamics and health assessment.