Fault size estimation of rolling bearing based on weak magnetic detection

Abstract

Spall is the main form of rolling bearing failure. To assess the bearing health status, this paper proposes a novel method based on the weak magnetic detection technology to estimate the spall size of the rolling bearing. The spall estimation model including the two key parameters of the roller motion speed and the entry-to-exit time is developed. They are both perception by the weak magnetic detection instead of the assumption. CEEMDAN combined with the de-trended fluctuation analysis (DFA) is introduced to identify the relevant mode including the key parameter information from the detected signal. Furtherly, the roller rotation speed and the entry-to-exit time are obtained by the feature spectrum based on the FFT and Hilbert transform, respectively. Comparison with the existing methods, the experimental result shows that the proposed method has a high performance and achieves less estimation bias.