Vibration displacement measurement method based on vision Gaussian fitting and edge optimisation for rotating shafts

Abstract

The vibrations of rotating machinery contain crucial information for monitoring the operational status of machines and diagnosing potential faults. Measuring vibration displacement using machine vision is currently a promising approach for machine monitoring in industries. However, the existing vision-based vibration displacement measurement methods cannot satisfy the high-accuracy requirements for detecting micrometre-level displacements generated by low-amplitude vibrations of rotating machinery. Therefore, this study proposes an improved noncontact-type vibration displacement measurement method based on Gaussian fitting and edge optimisation for rotating shafts. The real number-type edge positions of the rotating shafts within the selected region of interest (ROI) are accurately extracted via Gaussian edge fitting. Subsequently, edge-linking schemes, double-threshold detection and edge fitting are preformed to optimise the real number-type position of the edges. Finally, continuous displacement extraction of rotating shafts is achieved by monitoring the edge positions in the ROI. The coefficient of determination is used to evaluate a series of simulated and actual experiments quantitatively. The experimental results verify the feasibility, effectiveness, and robustness of the proposed method. An eddy current sensor is used in the actual experiments to verify the accuracy of the proposed method. The result shows that the coefficient of determination reaches 0.9792 when the amplitude of the measured displacement signals is only 22.7 μm.