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Hybrid EEMD-PCA and transformer-based deep learning approach for rotating bearing fault classification
Experimental validation shows that the proposed EEMD-PCA-Transformer method achieves 100% accuracy in identifying different bearing fault types, outperforming traditional techniques.
An improved CNN–Transformer–ELM framework with residual shrinkage networks for bearing fault diagnosis
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.