An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

Abstract

In recent years, transfer learning has been widely used in mechanical fault diagnosis with some achievements. However, most transfer learning methods do not perform well in diagnosis when the speed and load change simultaneously. Inspired by the adversarial learning mechanism, a transfer learning method named attention mechanism-guided domain adversarial network (AMDAN) is proposed in this paper. AMDAN regards the convolutional neural networks (CNNs) as the generator of the domain adversarial network to learn mutually invariant features and the domain classifier as the discriminator of the domain adversarial network. Attention mechanism is introduced to take into account the interchannel and intraspace feature fusion to improve the training efficiency. Then, multi-kernel maximum mean discrepancy (MK-MMD) is used to measure the distance of different feature spaces to achieve domain alignment. Finally, the superiority of AMDAN is verified by two sets of gear fault diagnosis experiments. The experimental results show that AMDAN has the highest classification accuracy and the strongest generalization ability compared with other methods.