A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model

In order to improve the diagnosis accuracy and generalization of bearing faults, an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method is proposed in this paper. Firstly, the discrete wavelet transform (DWT) was utilized to decompose the vibration signal into the subsignals in the different frequency bands, and then these different subsignals were transformed into a time-frequency representation (TFR) map by the continuous wavelet transform (CWT) method. Secondly, the TFR maps were input with respective to the multiple individual ViT models for preliminary diagnosis analysis. Finally, the final diagnosis decision was obtained by using the soft voting method to fuse all the preliminary diagnosis results. Through multifaceted diagnosis tests of rolling bearings on different datasets, the diagnosis results demonstrate that the proposed integrated ViT model based on the soft voting method can diagnose the different fault categories and fault severities of bearings accurately, and has a higher diagnostic accuracy and generalization ability by comparison analysis with integrated CNN and individual ViT.

Automated summary

This paper proposes an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method to improve the diagnosis accuracy and generalization of bearing faults. The vibration signal is decomposed using discrete wavelet transform (DWT) and transformed into a time-frequency representation (TFR) map using continuous wavelet transform (CWT). Multiple individual ViT models are employed for preliminary diagnosis analysis, and the final diagnosis decision is obtained through the soft voting method. Experimental results demonstrate the accuracy and generalization ability of the proposed model in diagnosing different fault categories and severities of bearings.