循环感知自编码器与跨信号一致性用于铁路车门异常检测
Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection
浏览论文内容
中文总结 AI 辅助
针对铁路车门异常检测，提出TCAA-CS循环感知自编码器，融合跨信号一致性，在真实数据上达到93.8%召回率与97.3%精确率，支持实时部署。
中文摘要 AI 辅助
乘客车门是铁路车辆中的安全关键子系统，然而在实际运行中检测异常车门行为具有挑战性，因为故障稀少、多样且通常无标签。本文将铁路车门状态监测视为一个循环级别的无监督异常检测问题，其中每个完整的开启-停留-关闭循环被视为一个单独的监测单元。我们提出了具有跨信号一致性的时间循环感知注意力自编码器（TCAA-CS），该模型仅在正常循环上进行训练。它结合了一个双流编码器，通过独立的1D-CNN分支处理连续的物理测量值（位置、电流、电压）和二元逻辑状态（车门关闭、车门锁定），一个带有时间注意力池化的LSTM编码器，以及一个融合重建误差、潜在空间偏差和相位感知跨信号一致性的三重混合异常分数。一致性项有助于识别单个信号看似合理但其间关系在物理或逻辑上变得不一致的情况。在来自商业运营客运列车的真实工业数据上，TCAA-CS达到了93.8%的召回率、97.3%的精确率和0.5%的误报率，优于代表性的无监督基线方法。在NVIDIA Jetson AGX Xavier上的系统级评估支持实时车载部署的可行性。
英文摘要
Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging because faults are rare, diverse, and often unlabeled. This paper addresses railway door condition monitoring as a cycle-level unsupervised anomaly detection problem, where each complete opening-dwell-closing cycle is treated as a single monitoring unit. We propose the Temporal Cycle-Aware Attention Autoencoder with Cross-Signal Consistency (TCAA-CS), trained exclusively on nominal cycles. It combines a dual-stream encoder that processes continuous physical measurements (position, current, voltage) and binary logical states (door-closed, door-locked) through separate 1D-CNN branches, an LSTM encoder with temporal attention pooling, and a triple hybrid anomaly score fusing reconstruction error, latent-space deviation, and phase-aware cross-signal consistency. The consistency term helps identify cases where individual signals appear plausible but their inter-signal relationships become physically or logically inconsistent. On real industrial data from a passenger train in commercial service, TCAA-CS achieves 93.8% recall, 97.3% precision, and a 0.5% false-alarm rate, outperforming representative unsupervised baselines. System-level evaluation on an NVIDIA Jetson AGX Xavier supports the feasibility of real-time onboard deployment.
发表机构
- Université Polytechnique Hauts-de-France（上法兰西理工大学）
- Alstom（阿尔斯通）
- University of Sharjah（沙迦大学）
机构由 AI 辅助整理，请以论文原文为准。
