Fault diagnosis of planetary gears in noisy environments using a VMTransformer model

Abstract As a green and renewable energy source, wind energy experienced considerable growth, offshore wind power technology is increasingly valued by nations worldwide. Planetary gearboxes are critical but failure-prone components in wind turbines, which present a significant risk to the operational stability of the entire system. To address the issue of noise interference in the vibration signals of gearboxes under extreme conditions, this paper proposes the VMTransformer model, which integrates, variational mode decomposition (VMD) and the multichannel transformer for the fault diagnosis of planetary gear in wind turbine generators. The noise in the vibration signal is decomposed and reconstructed in the frequency domain using the VMD, and the significant frequency information is retained for signal noise reduction. The results of the decomposition are subjected to multi-scale feature fusion over multiple channels, which can maximize the frequency domain information provided by the VMD to further refine and enhance the model’s ability to extract important features in the signal. Efficient channel attention is incorporated into the transformer model to suppress redundant information, enhancing the extraction of relevant information from each channel, and improving both the model’s stability. The experimental results demonstrate that the model proposed in this paper obtains an accuracy of 98.62%, which is a significant improvement over other models, and the model also performs well in noisy environment.