3D structural vibration identification from dynamic point clouds

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Department of Mechanical Engineering-Engineering Mechanics


Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure's displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems.

Publication Title

Mechanical Systems and Signal Processing