2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2025)

This paper proposes a method for accurately measuring the angular velocity of rotating machinery based on corner transformation and designs a corresponding sensor system. The system primarily consists of a grating and a high-speed camera. By attaching a specific rectangular pattern to the shaft surface, a phase-modulated signal is generated during the shaft's rotation. The instantaneous angular velocity (IAS) is then extracted using image feature tracking and corner detection algorithms. Unlike traditional encoders, which rely on resolution, this method theoretically enables angular velocity detection with infinite resolution, making it particularly suitable for condition monitoring at low speeds or even near-zero speed. This paper first establishes a rotating shaft model in a simulation platform to simulate the sensor imaging process and validate the method's feasibility through corner detection. An experimental testbed is then constructed to test the sensor under constant and variable speed conditions. Experimental results demonstrate that this method maintains high detection accuracy at extremely low speeds and during dynamic acceleration and deceleration, with the measurement results highly consistent with the motor's setpoints. Further error analysis reveals that the grating attachment accuracy, lighting conditions, and mechanical vibration have some influence on the measurement results, but image preprocessing and feature normalization methods can effectively reduce these errors. Research shows that this method not only has high accuracy and stability, but also has the advantages of simplicity, low cost, and a wide application range. It can provide a new technical approach for condition monitoring, process control, and predictive maintenance of rotating machinery.

Rotating machinery, instantaneous angular parameters, machine vision, image coding, signal processing