Fault diagnosis of ball mill rolling bearing based on multi-feature fusion and RF

The diagnosis effect is unsatisfactory because it is challenging to extract high-quality fault characteristics from a single signal given the complicated working conditions of the metallurgical industry. Aiming at the problem of directly using current and vibration signals for fusion, which cannot reflect the advantages of the two types of signals in different frequency bands and the complementary information between each other, but affects the diagnostic performance, this paper proposes a multi-feature complementary fusion fault diagnosis method based on vibration and current signals. First, the high-frequency coefficient features of the vibration signal and the current signal are fused through the maximum absolute value rule to form complementary features that reflect the high-frequency characteristics. The low-frequency coefficient features of the vibration signal and the current signal are fused through sparse representation (SR) to form complementary features that reflect the low-frequency features. By defining a feature matrix composed of multiple features to fuse full frequency band features, the global feature characterization capability is enhanced. After feature fusion, redundant features are removed to increase classification accuracy and categorize the bearing defect state using a combination of random forest (RF) and recursive feature elimination. Experimental results show that this method is more accurate than the diagnosis results based on vibration signals and current signals. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.