TL;DR
Experimental validation shows that the proposed EEMD-PCA-Transformer method achieves 100% accuracy in identifying different bearing fault types, outperforming traditional techniques.
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.
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.
Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential dia...
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
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.
A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.
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