A novel spindle fault classification network based on WDCNN-1DViT-PFEN of CNC machine tool under multiple working conditions

Abstract Diagnosing spindle failures in CNC machine tools poses challenges due to data imbalance and multi-condition complexity. With normal samples vastly outnumbering failure samples, traditional models struggle to effectively identify rare-class failures. Insufficient feature extraction under multiple working conditions further compromises diagnostic accuracy. To address these issues, a parallel feature extraction network (PFEN) is proposed. This approach utilizes deep convolutional neural networks with wide first-layer kernel (WDCNN) to extract coarse-grained features from vibration signals. It innovatively designs a one-dimensional convolutional neural network (1DCNN) with Vision transformer (1DViT), integrating a parallel structure of 1DCNN and Transformer encoders to balance local details and global dependency features. A soft attention-based feature fusion method effectively combines WDCNN and 1DViT features, enhancing key feature representation. To address the issue of traditional loss functions overemphasizing majority classes and neglecting minority classes under imbalanced data conditions, this paper introduces a joint mechanism of weighted cross-entropy loss and contrastive loss. By dynamically adjusting weighting factors to enhance the importance of minority class samples, the model better learns rare and challenging features, thereby improving sensitivity to subtle differences in complex working conditions. Finally, experimental validation on the CK210sp CNC machine tool spindle imbalance fault dataset demonstrates that the proposed method (WDCNN-1DViT-PFEN) achieves superior diagnostic accuracy compared to existing methodologies while maintaining stable diagnostic performance across multiple working conditions. The results validate the effectiveness of this approach in addressing sample imbalance issues in CNC machine tool spindle fault diagnosis.