P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings

The axle-box bearing is a critical load-bearing component in high-speed trains and is prone to failure under long-term heavy-duty operation, affecting both operational efficiency and safety. Current deep-learning-based fault diagnosis methods face two key challenges: difficulty in capturing temporal features across multiple scales simultaneously, and limited capability in modeling local sequential patterns. To address these issues, we propose P2IFormer, a fault diagnosis model based on multi-granularity patch-to-image embedding. The raw vibration sequence is divided into equal-length patch sequences under multiple granularities, each defined by a fixed window size. Each patch is then transformed into a Gramian Angular Field (GAF) image to extract spatial features and generate granularity-specific embedding. A multi-granularity self-attention mechanism is used to model both intra- and inter-granularity dependencies. The resulting multi-granularity features are fused and fed into a softmax classifier for final fault prediction. Experiments conducted under four constant-speed conditions and one variable-speed condition demonstrate that P2IFormer achieves over 99.5% accuracy across all scenarios, significantly outperforming existing CNN- and Transformer-based methods.