Train Axlebox Bearing Fault Diagnosis Based on MSC–SGMD

Abstract

Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high–quality train performance. In this paper, based on the traditional symplectic geometry mode decomposition (SGMD) algorithm, a maximum spectral coherence signal reconstruction algorithm is proposed to extract the intrinsic connection between the SGMD components with the help of the frequency domain coherence idea and reconstruct the key signal components so as to effectively improve the extraction of composite fault features of axlebox bearings under different speed conditions. Firstly, based on the traditional SGMD algorithm, the vibration signal of the axle box is decomposed to extract its symplectic geometry components (SGCs). Secondly, the spectral coherence coefficient between the SGCs is calculated, and the signal in which the maximum value is located is taken as the key component for the additive reconstruction Finally, the envelope spectrum is used to extract the reconstructed signal fault features. The inner race, outer race, and compound bearing failure vibration signal acquisition under different speed conditions were carried out on the equal scale axlebox bearing failure simulation test bench, and the effectiveness of the proposed algorithm was verified based on the axlebox vertical acceleration signal.