Small sample gearbox fault diagnosis method based on a frequency band attention network

Deep learning-based fault diagnosis methods typically require large amounts of fault data. To enable accurate gearbox fault diagnosis in small-sample scenarios, a novel diagnosis method based on a frequency band attention network is proposed.

First, a reconstruction-encoding layer is used to transform vibration signals into sub-band encoded signals that are more suitable for classification. Then, an intrinsic band attention layer is designed to effectively extract salient time-frequency features from the sub-band encoded signals. Finally, a multi-feature fusion module is used to integrate the extracted time-frequency features for fault recognition in small-sample conditions.

Experimental results on a gearbox fault simulation platform show that the proposed method achieves a fault diagnosis accuracy of 99.85% in small-sample conditions, surpassing existing benchmark models.

These findings can provide a valuable reference for gearbox fault diagnosis in small-sample conditions.