基于频带注意力网络的齿轮箱小样本故障诊断方法OA

[Objective]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.[Method]First,a reconstruction-encoding layer is used to transform vibration signals into sub-band encoded signals that are more suitable for classification.Then,an in-trinsic 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 fea-tures for fault recognition in small-sample conditions.[Results]Experimental results on a gearbox fault simul-ation platform show that the proposed method achieves a fault diagnosis accuracy of 99.85%in small-sample conditions,surpassing existing benchmark models.[Conclusion]These findings can provide a valuable ref-erence for gearbox fault diagnosis in small-sample conditions.