用深度学习提前预警轨道电路故障，准确率达99.3%
CVCM Track Circuits Pre-emptive Failure Diagnostics for Predictive Maintenance Using Deep Neural Networks
- 基于深度神经网络分析异常信号，实现故障前预测
- 在异常出现1%时即可检测，整体准确率99.31%
- 支持不确定性估计，适合高安全铁路系统部署
轨道电路是铁路运行的关键信号子系统，用于定位列车。连续变电流调制（CVCM）是其中一种技术。作为现场部署的高安全要求设备，其故障常由渐进式微小异常引发，常规方法因依赖明显信号变化难以早期发现。本文提出一种基于深度神经网络的预测性维护框架，可在故障演变为严重问题前识别异常类型。在10个不同安装点的CVCM故障案例上验证，方法符合ISO-17359标准，整体准确率达99.31%，检测时间距异常起始仅1%。通过置信度预测提供不确定性估计，各类别覆盖率均达99%以上。鉴于CVCM全球广泛应用，该方法可扩展至其他轨道电路与铁路系统，提升运行可靠性。
原文摘要 · Abstract (English)
Track circuits are critical for railway operations, acting as the main signalling sub-system to locate trains. Continuous Variable Current Modulation (CVCM) is one such technology. Like any field-deployed, safety-critical asset, it can fail, triggering cascading disruptions. Many failures originate as subtle anomalies that evolve over time, often not visually apparent in monitored signals. Conventional approaches, which rely on clear signal changes, struggle to detect them early. Early identification of failure types is essential to improve maintenance planning, minimising downtime and revenue loss. Leveraging deep neural networks, we propose a predictive maintenance framework that classifies anomalies well before they escalate into failures. Validated on 10 CVCM failure cases across different installations, the method is ISO-17359 compliant and outperforms conventional techniques, achieving 99.31% overall accuracy with detection within 1% of anomaly onset. Through conformal prediction, we provide uncertainty estimates, reaching 99% confidence with consistent coverage across classes. Given CVCMs global deployment, the approach is scalable and adaptable to other track circuits and railway systems, enhancing operational reliability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。