Acoustic Emission Diagnosis of Rolling Bearing Faults based on Optimized VMD-Transformer

The acoustic emission signal of rolling bearing fault has poor feature data extraction effect, resulting in low fault diagnosis accuracy. This paper proposes a rolling bearing fault diagnosis method based on optimized variational mode decomposition (VMD) denoising and feature enhancement, and using the Transformer model for diagnosis. The Seahorse Optimizer (SHO) algorithm is used to optimize the VMD parameters [K, α]. The optimized VMD is used to decompose the acoustic emission signal of the bearing to obtain several intrinsic mode components (IMFs). The intrinsic mode components containing more fault information are selected according to the envelope entropy and kurtosis, and the signal is reconstructed; the reconstructed signal is input into the Transformer model to complete the diagnosis. The experimental results show that the accuracy of the optimized VMD-Transformer fault diagnosis model reaches 98.1%, which is significantly improved compared with other fault diagnosis models.