A Transfer-Learning Fault Diagnosis Method Considering Nearest Neighbor Feature Constraints

Aiming at the problem of low diagnostic accuracy of fault diagnosis models due to changes in actual operating conditions, a novel fault diagnosis method based on transfer learning considering nearest neighbor feature constraints is proposed. First, nearest neighbor samples are considered to measure data features. In addition, a nearest neighbor feature constraint strategy is designed to improve the feature extraction performance of the network. Second, a multiple-alignment strategy of nearest neighbor samples is proposed to enhance the domain adaptation performance of the network model utilizing multiple alignments. Then, a loss function dynamic weight strategy is used to improve the convergence of the loss function during model training. Finally, the experimental verification is carried out on the public data set of the Western Reserve University and the private data set. The experimental results show that the proposed method exhibits superior transfer performance with reliability and stability compared to the existing methods.