Using multi-sine excitation and rigid body motion compensation in randomly sampled camera-based experimental modal analysis to improve SNR

Camera-based Experimental Modal Analysis (EMA) is able to measure full-field vibration modes contactlessly. However, camera measurements still suffer from a lower sampling frequency and lower Signal-to-Noise Ratio (SNR) compared to accelerometers and laser vibrometers. Since regular sampling confines the results to the Nyquist frequency (i.e., half the frame rate of a camera), in a previous paper we have proposed an approach that exploits images randomly sampled in time, allowing to measure modes beyond the Nyquist frequency. In this paper we employ such an approach and we propose a novel excitation scheme to increase the SNR, i.e., the specimen under investigation is excited at selected resonance frequencies, which results in high amplitudes of the structural responses. These frequencies are measured by accelerometers during a pre-test, and a multi-sine signal containing these frequency components is generated as structural excitation through a shaker. Furthermore, as Rigid Body Motion (RBM) due to pseudo-free suspension is not desired for EMA, in this paper we also propose a novel optimization workflow for RBM compensation. The accuracy of the proposed approach with multi-sine excitation is first evaluated numerically by reconstructing steady-state response signals. Secondly, the whole workflow (i.e., multi-sine excitation and RBM compensation) is validated on an experimental case with a 3D component and a stereo camera setup. From a sequence of randomly sampled images equivalent to a frame rate below 50 fps (corresponding to an equivalent Nyquist frequency below 25 Hz), four modes up to 250 Hz (i.e., ten times higher than the Nyquist frequency) are extracted, which correlate well with the modes predicted through a finite element simulation.