用Transformer预测轨道轴振动，提前预警故障，助力铁路智能维护
Transformer Vibration Forecasting for Advancing Rail Safety and Maintenance 4.0
- 设计ShaftFormer模型，用Transformer处理时序振动数据
- 通过模拟多种工况振动信号，降低实验数据采集成本
- 适合铁路运维与智能维护领域研究人员参考
铁路轴系维护对防止严重事故和经济损失至关重要。铁路行业正从传统定期检查转向基于先进状态监测的 Maintenance 4.0。本文提出一种深度自回归方法，可无缝集成至现有系统，预防机械故障。通过模拟不同工况和故障场景下的振动信号，提升数据集鲁棒性，从而增强检测系统的有效性。该方法利用列车轴上加速度计获取的实验振动信号，核心贡献包括专为时序数据设计的Transformer模型ShaftFormer，以及结合谱方法与增强观测模型的替代方案。由于铁路振动信号具有非平稳特性（受速度、载荷变化影响），本研究有效应对这些复杂性，为铁路预测性维护提供有力工具。
原文摘要 · Abstract (English)
Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniques to enhance safety and efficiency, moving beyond traditional periodic inspections toward Maintenance 4.0. This study introduces a robust Deep Autoregressive solution that integrates seamlessly with existing systems to avert mechanical failures. Our approach simulates and predicts vibration signals under various conditions and fault scenarios, improving dataset robustness for more effective detection systems. These systems can alert maintenance needs, preventing accidents preemptively. We use experimental vibration signals from accelerometers on train axles. Our primary contributions include a transformer model, ShaftFormer, designed for processing time series data, and an alternative model incorporating spectral methods and enhanced observation models. Simulating vibration signals under diverse conditions mitigates the high cost of obtaining experimental signals for all scenarios. Given the non-stationary nature of railway vibration signals, influenced by speed and load changes, our models address these complexities, offering a powerful tool for predictive maintenance in the rail industry.
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