A Multi-Task Strategy Integrating Multi-Scale Fusion for Bearing Temperature Prediction in High-Speed Trains Under Variable Operating Conditions

In this paper, the concept of symmetry is utilized to inform the structural design of our multi-sensor fusion framework—that is, the hierarchical feature extraction and spatial–temporal correlation modeling exhibit symmetrical properties across sensor nodes and temporal scales. Monitoring bearing temperature in high-speed train bogies is crucial for assessing system health and ensuring operational safety. Accurate temperature prediction facilitates proactive maintenance. However, existing models struggle to capture multi-scale temporal patterns, long-term dependencies, and spatial correlations among bearings, and they often overlook varying operating conditions. To address these challenges and enhance prediction accuracy in real-world operations, this study proposes MSC-Ada-MTL, a novel framework that integrates multi-scale feature extraction and operating condition recognition through adaptive multi-task learning. The approach employs multi-scale hierarchical temporal networks (MSHNets) to capture temporal features across different scales from multiple bogie sensors. A speed-based recognition strategy classifies operating conditions to enhance model reliability and simplify prediction tasks. By leveraging multi-task learning, the framework simultaneously models temporal dynamics and spatial correlations, creating a comprehensive prediction model. Validation and ablation experiments demonstrate significant improvements in prediction accuracy and robustness across diverse operating scenarios. The proposed method effectively addresses the limitations of existing approaches by synergistically combining temporal multi-scale analysis, operational condition awareness, and spatial–temporal relationship modeling, providing enhanced adaptability for real-world railway maintenance applications.