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.

翻译：本文提出一种深度学习框架，用于分析基础设施健康监测中的车载振动响应信号。所提出的WaveletInception-BiGRU网络采用可学习小波包变换进行早期频谱特征提取，随后通过一维Inception残差网络模块进行多尺度高级特征学习。双向门控循环单元模块进一步整合时序依赖性并纳入运行条件（如测量速度）。该方法能够有效分析不同速度下记录的振动信号，无需显式的信号预处理步骤。序列估计头充分利用双向时序信息，生成精确的局部化基础设施健康评估。最终，该框架生成高分辨率健康剖面，并将其空间映射至基础设施的物理布局。通过实测数据进行的轨道刚度回归与过渡区分类案例研究表明，所提框架显著优于现有先进方法，彰显了其在实现精确、局部化、自动化车载基础设施健康监测方面的潜力。