关键词: 深度学习, 故障诊断, 小样本, 原型网络, 混合自注意力机制

Abstract: The scarcity of labeled fault sample data of wind turbine gearboxes in some wind farms seriously reduced the accuracy of fault diagnosis. To solve this issue, a fault diagnosis method based on mixed self-attention prototype networks under small samples was proposed. First, the vibration signals were mapped to the fault feature measurement space through the prototype networks. Then, the position self-attention mechanism and channel self-attention mechanism were used for matrix fusion to construct a mixed self-attention module, which established the global dependence of the original vibration signals and obtained more discriminative characteristic information to learn the measurement prototypes of wind power gearboxes in various health states. Finally, the trained metric classifier was adopted to identify the faults of the wind turbine gearbox under the condition of small samples. Experimental results show that the fault diagnosis method of the mixed self-attention prototype networks may achieve high-precision fault diagnosis of wind turbine gearboxes on different scales of small sample datasets.

Key words: deep learning, fault diagnosis, small sample, prototype network, mixed self-attention mechanism