Cross-domain semi-supervised stacked broad learning network for bearing fault diagnosis

Integrating self-supervised learning with domain adaptation based on Multi-view Structural Consistency and joint learning for bearing fault diagnosis

Rotary machinery is an indispensable part of modern industry; it greatly reduces labor and improves production efficiency. Therefore, accurate diagnosis and early warning of faults help minimize repair costs, increase efficiency, and prevent problems. Deep learning-based fault diagnostic methods often assume training...

Bearing fault diagnosis based on multiscale subdomain full-dimensional dynamic convolutional networks

Experimental results demonstrate that the proposed MSODNet model effectively captures both the divergence and commonality of signals under multi-condition scenarios, achieving an average accuracy of 99.35% in bearing vibration fault diagnosis, confirming the superior diagnostic performance and robustness of the MSODNet...

An intelligent cross-domain fault diagnosis framework based on XGBoost and CNN-KLD for multi-source data

To address the limitations in model generalization caused by discrepancies in the distribution of multi-source data and insufficient labeling in the fault diagnosis of high-speed train bearings, this paper constructs an intelligent diagnostic computing pipeline designed for cross-operating-condition scenarios. This iss...

Domain generalization network for intelligent fault diagnosis based on low-rank Gaussian feature learning

A noise-robust hybrid deep learning network for bearing fault diagnosis

Multi-Structure Fusion Conditional Domain Adaptation for Bearing Fault Diagnosis

To address the limited feature extraction capability of single-architecture deep models, pronounced distribution discrepancies in bearing vibration signals, and instability during cross-domain training under varying operating conditions, a multi-structure fusion conditional domain adaptation (MSFCDA) method is proposed...