【摘要】 道岔转辙机的稳定运行是高铁行车安全的关键保障，而高铁系统的智能化发展对其工作状态的精准感知与自主诊断能力提出了更高要求.为克服传统方法在诊断精度、计算实时性及抗干扰能力等方面的局限性，构建了一种融合跨模态特征注意力机制与Transformer-XL递归记忆机制的智能诊断模型，以提升现场复杂运营条件下的故障识别能力与环境适应性.通过引入跨模态注意力机制，实现功率曲线信号与尖轨振动信号的动态特征交互，避免单模态信息缺失导致的判断偏差；采用Transformer-XL的递归记忆机制，动态调整模型对历史信息的感知范围，使模型能够跨时间窗口提取历史状态信息；结合1D-CNN进行短时动态特征提取，优化全局时序建模，在增强抗噪鲁棒性的同时降低计算复杂度.实验结果表明：该模型在跨模态特征表达、长时依赖建模、抗噪鲁棒性和计算效率等方面均具有显著优势，为高铁智能运维提供了一种兼具高效性、低资源消耗与强环境适应性的智能诊断方案，推动故障处置由被动维修向预测性维护转变，提升高铁运行的安全裕度与稳健性.更多还原

【Abstract】 The stable operation of switch machines is a key guarantee for the safety of High-Speed Railways(HSRs). With the increasing demand for intelligent railway systems, higher requirements are imposed on the precise perception and autonomous diagnosis of switch machine working conditions. To overcome the limitations of traditional methods in terms of diagnostic accuracy, computational realtime performance, and anti-interference capability, this paper constructs an intelligent diagnosis model that integrates cross-modal feature attention mechanism and Transformer-XL recursive memory mechanism. The proposed model enhances fault recognition and environmental adaptability under complex operating conditions. By introducing a cross-modal attention mechanism, it enables dynamic interaction between power curve signals and switch rail vibration signals, mitigating the judgment bias caused by missing single-modal information. The Transformer-XL with recursive memory mechanism dynamically adjusts the model’s perception of historical information, allowing it to extract state information across time windows. Additionally, 1D-CNN is incorporated for short-term dynamic feature extraction, optimizing global sequential modeling while improving noise robustness and reducing computational complexity. Experimental results demonstrate that the proposed model exhibits significant advantages in cross-modal feature representation, long-term dependency modeling, noise robustness, and computational efficiency. This study provides an intelligent diagnostic solution for HSR maintenance, featuring high efficiency, low resource consumption, and strong environmental adaptability. It facilitates the transition from reactive maintenance to predictive maintenance, thereby improving operational safety margins and robustness.更多还原