Abstract: Aiming at the performance limitation of the mechanical equipment fault diagnosis, a method of fault diagnosis for equipment based on domain adversarial neural networks (DANN) was proposed. The accuracy of diagnosis was effected by the distribution of training and testing data due to the complex and changeable operating conditions when using traditional machine learning methods. Based on the convolutional neural networks, the diagnosis model was established consisting of feature extractor, fault classifier and domain discriminator. The diagnosis was conducted by minimizing the loss of fault classifier and maximizing the loss of domain discriminator. The fault identification ability was proved in the diagnosis experiments of bearing fault with the comparison of other methods. The results indicate that the proposed method has an average accuracy higher than 96%, and it is not affected by the differences between training and testing data.
Key words: fault diagnosis; domain adversarial neural networks（DANN）; bearing fault; network diagnosis