An improved CNN–Transformer–ELM framework with residual shrinkage networks for bearing fault diagnosis

Multiview Scalogram Fusion With Adaptive SE-CNNs for Bearing Fault Diagnosis

A Rolling Bearing Fault Diagnosis Method Based on Scaled Dot-Product Attention

A novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network, which maintains an exceptional diagnostic accuracy even under severe background noise.

DWAN: a dictionary-wavelet attention network for noise-robust bearing fault diagnosis

Experiments demonstrate that the proposed denoising diagnostic framework, termed the dictionary-wavelet attention network, provides stronger noise robustness than mainstream deep-learning diagnostic models.

MSFormer: Multi-Scale Transformer for Robust Fault Diagnosis of Machines Under Complex Conditions

The proposed MSFormer incorporates a parallel multi-scale Convolutional Neural Network architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals to provide a powerful and precise intelligent solution for mechanical fault diagnosis.

Noise-Robust Multi-Domain Feature Integration for Bearing Fault Diagnosis

A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.

Domain-generalized multi-channel fusion for bearing fault diagnosis under variable operating conditions

Experiments demonstrate that this method can diagnose bearing failures under cross-conditions—even when trained solely on source-domain data and without exposure to target-domain data during training—and that its DG accuracy outperforms that of existing mainstream advanced methods.