用小波网络分析车载振动信号，实现高精度桥梁健康状态定位
WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring
- 结合可学习小波变换与双向GRU，自动提取多尺度时频特征
- 在真实轨道数据上实现刚度回归与过渡区分类，精度显著超越现有方法
- 适合需要自动化、精准定位的铁路等基础设施实时监测场景
本文提出一种用于车载振动信号分析的深度学习框架，用于基础设施健康监测。所提出的WaveletInception-BiGRU网络采用可学习小波包变换（LWPT）进行早期频谱特征提取，随后通过一维Inception-残差网络（1D Inception-ResNet）模块实现多尺度高层特征学习。双向门控循环单元（BiGRU）模块则整合时间依赖性，并融入运行速度等工况条件。该方法可有效分析不同速度下采集的振动信号，无需显式预处理。序列估计头进一步利用双向时间信息，实现高精度、局部化的结构健康评估。最终，该框架生成空间映射至物理布局的高分辨率健康图谱。基于真实轨道测量数据的案例研究显示，该框架在轨枕刚度回归与过渡区分类任务中显著优于现有最优方法，展现出在精准、局部化、自动化车载基础设施健康监测中的巨大潜力。
原文摘要 · Abstract (English)
This paper presents a deep learning framework for analyzing on board vibration response signals in infrastructure health monitoring. The proposed WaveletInception-BiGRU network uses a Learnable Wavelet Packet Transform (LWPT) for early spectral feature extraction, followed by one-dimensional Inception-Residual Network (1D Inception-ResNet) modules for multi-scale, high-level feature learning. Bidirectional Gated Recurrent Unit (BiGRU) modules then integrate temporal dependencies and incorporate operational conditions, such as the measurement speed. This approach enables effective analysis of vibration signals recorded at varying speeds, eliminating the need for explicit signal preprocessing. The sequential estimation head further leverages bidirectional temporal information to produce an accurate, localized assessment of infrastructure health. Ultimately, the framework generates high-resolution health profiles spatially mapped to the physical layout of the infrastructure. Case studies involving track stiffness regression and transition zone classification using real-world measurements demonstrate that the proposed framework significantly outperforms state-of-the-art methods, underscoring its potential for accurate, localized, and automated on-board infrastructure health monitoring.
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