The existing bearing temperature fault detection and early warning system has a high false alarm rate and insufficient early warning ability. For this reason, in this study, a method for detecting the abnormal bearing temperature of high-speed trains based on spatiotemporal fusion decision-making was proposed. First, the temperature characteristics of similar bearings were compared and analyzed with different spatial distributions. Then, a bearing abnormal temperature rise detection model based on the analytic hierarchy process (AHP) entropy method was proposed. Second, the temperature characteristics of the same bearings were compared and analyzed with different time distributions. A real-time prediction model of high-speed train bearing temperature anomalies based on Bi-directional Long Short-Term Memory (BILSTM) was proposed. Finally, the D-S evidence theory was used to combine the anomaly detection model based on the AHP entropy method and the anomaly detection model based on BILSTM real-time prediction. Through the comprehensive diagnosis and decision-making of high-speed train bearings from two dimensions of space and time, a more comprehensive and accurate anomaly detection model was realized. The experimental results showed that the spatiotemporal comparison fusion decision model successfully eliminated the misjudgment phenomenon of single-dimension model diagnosis and that it has good early warning ability.

现有的轴承温度故障检测预警系统误报率高，预警能力不足。为此，本研究提出了一种基于时空融合决策的高速列车轴承异常温度检测方法。首先，对比分析了不同空间分布下同类轴承的温度特性。然后，提出了一种基于层次分析法(AHP)熵方法的轴承异常温升检测模型。其次，对相同轴承在不同时间分布下的温度特性进行比较分析。提出了一种基于双向长短期记忆(BILSTM)的高速列车轴承温度异常实时预测模型。最后，采用DS证据理论将基于AHP熵方法的异常检测模型和基于BILSTM实时预测的异常检测模型结合起来。通过从空间和时间两个维度对高速列车轴承进行综合诊断和决策，实现了更全面、更准确的异常检测模型。实验结果表明，时空比较融合决策模型成功消除了单维模型诊断的误判现象，具有良好的预警能力。通过从空间和时间两个维度对高速列车轴承进行综合诊断和决策，实现了更全面、更准确的异常检测模型。实验结果表明，时空比较融合决策模型成功消除了单维模型诊断的误判现象，具有良好的预警能力。通过从空间和时间两个维度对高速列车轴承进行综合诊断和决策，实现了更全面、更准确的异常检测模型。实验结果表明，时空比较融合决策模型成功消除了单维模型诊断的误判现象，具有良好的预警能力。