Intelligent Fault Diagnosis Method Based on Hybrid Attention and Dimension‐Aligned Distillation for Rotating Machinery

Intelligent fault diagnosis (IFD) of rotating machinery is critical for ensuring industrial safety and reliability. However, existing deep learning‐based IFD methods face three core challenges: suboptimal feature discrimination of single attention mechanisms, high computational cost limiting edge deployment, and class imbalance bias leading to misdiagnosis of rare faults. This study proposes an engineering‐friendly IFD method integrating wavelet time–frequency analysis, convolutional block attention module (CBAM)‐multihead self‐attention (MHSA) hybrid attention, dimension‐aligned lightweight distillation, and class‐balanced weighted loss fusion. First, the continuous wavelet transform (CWT) converts raw vibration signals into time–frequency graphs for effective feature extraction. Second, the teacher model is designed by pretrained ResNet18, and the student model is designed by pretrained MobileNetV3‐Small to extract feature map. Third, a CBAM‐MHSA hybrid attention module is designed to complementarily capture local frequency enhancement and global temporal‐spatial correlations, improving fault feature discrimination. Furthermore, a dimension‐aligned teacher–student distillation framework is proposed to compress the model into a lightweight version, solving the high computational cost problem of traditional deep models. Finally, a class‐balanced weighted loss fusion strategy is developed to mitigate class imbalance bias. Experimental results on two public datasets (SEU fault dataset and Case Western Reserve University [CWRU] bearing dataset) show that the proposed method achieves perfect classification (accuracy [Acc] = 1.0000, F1‐macro = 1.0000) on both datasets, with 86.6% fewer parameters. It provides a practical solution for industrial IFD.