A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

Abstract

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 (NNFCTL) is proposed. Firstly, nearest neighbor samples are considered to measure data features, as well as a nearest neighbor feature constraint strategy is designed to improve the feature extraction performance of the network. Secondly, 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 weighting 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 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 with the existing methods.

Adaptive Dynamic Threshold Graph Neural Network: A Novel Deep Learning Framework for Cross-Condition Bearing Fault Diagnosis

“…Huang et al [18] proposed a deep adversarial capsule network designed for comprehensive fault diagnosis in industrial equipment. Zeng et al [19] introduced a transfer-learning-based fault diagnosis approach that integrates nearest neighbor feature constraints, specifically tailored for addressing cross-condition fault diagnosis challenges. However, if the differences between two domains are substantial, a model trained on the source domain may struggle to adapt well to the data distribution of the target domain.…”