A Synergistic Fault Diagnosis Method for Rolling Bearings: Variational Mode Decomposition Coupled with Deep Learning

To address the limitations of the traditional methods that are used to extract features from non-stationary signals and capture temporal dependency relationships, a rolling bearing fault diagnosis method combining variational mode decomposition (VMD) and deep learning is proposed. A hybrid VMD-CNN-Transformer model is constructed, where VMD is used to adaptively decompose bearing vibration signals into multiple intrinsic mode functions (IMFs). The convolutional neural network (CNN) captures the local features of each modal time series, while the multi-head self-attention mechanism of the Transformer captures the global dependencies of each mode, enabling the global analysis and fusion of features from each mode. Finally, a fully connected layer is used to classify the 10 fault types. The experimental results on the Case Western Reserve University bearing dataset demonstrate that the model achieves a fault diagnosis accuracy of 99.48%, which is significantly higher than that of single or traditional combined methods, providing a new technical path for the intelligent diagnosis of rolling bearing faults.