Research on rotational speed detection of rolling bearing cage based on speckle images

Purpose This study aims to address the limitations of contact sensors and the deployment difficulties of deep learning models in measuring the rotational speed of rolling bearing cages. It proposes a lightweight, non-contact visual detection method to achieve robust speed monitoring under complex operating conditions like severe vibration. Design/methodology/approach The method combines customized speckle patterns with a cascaded noise-robust matching algorithm. High-contrast matte speckle markers are applied to the cage to enhance texture. The Scale-Invariant Feature Transform (SIFT) algorithm extracts features and resolves scale drifts caused by axial runout. Following preliminary matching via Fast Library for Approximate Nearest Neighbors (FLANN), the Progressive Sample Consensus (PROSAC) algorithm is used to swiftly eliminate mismatching points induced by oil film reflections. Findings Experimental tests demonstrated the method’s high precision on a test bench. Within a speed range of 500–3000 r/min, the system effectively overcame motion blur, maintaining a maximum relative error between measured and theoretical speeds within 2.39%. Originality/value The originality lies in the hardware-algorithm synergistic design, avoiding reliance on the massive training data required by deep learning. By integrating customized speckle markers with an optimized PROSAC-based registration framework, this study provides a highly robust, low-cost and easily deployable tool for dynamic performance testing of rolling bearings.