Time- and Frequency-Domain Dual-Stream Contrastive Learning for High-Speed Train Axle-Box Bearing Fault Diagnosis

Axle-box bearings are safety-critical components in high-speed electric multiple units, and their vibration responses are affected by wheel–rail excitation, load variation, and environmental noise. Existing deep-learning diagnosis methods often rely on either raw time-series signals or time-frequency images, which limits their ability to exploit complementary temporal and spectral fault cues. This paper proposes Time-Frequency Dual-Stream Contrastive BearingNet (TFDC-BearingNet). The core idea is that a single vibration signal can be represented in two perceptually complementary forms, a global composite time- and frequency-domain visual view and a local temporal waveform view, and the global representation can serve as a query to guide the selection of fault-discriminative local signal components. Specifically, a visual branch extracts global representations from composite time-domain and frequency-domain images with a pre-trained Vision Transformer, whereas a signal branch uses a multi-scale tokenizer and Transformer encoder to preserve local temporal dependencies from raw sequences. A global-guided local selection module further uses cross-attention and gated fusion to select diagnosis-relevant signal components under visual guidance, and a contrastive objective aligns the two views in a shared semantic space. Experiments on a high-speed train rolling test-rig dataset and the public Southeast University bearing dataset achieve accuracies of 98.78% and 99.95%, respectively. Bidirectional cross-condition experiments on the SEU dataset show target accuracies of 94.77% and 93.46%. Comparative, ablation, cross-condition, noise-robustness, feature-visualization and statistical analyses indicate that the proposed dual-stream alignment and global-guided local selection improve diagnostic accuracy.