To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48 %. The recognition performance of the proposed model is compared and verified.

中文翻译：

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基于车辆多元动态响应和深度学习的无砟轨道层间间隙检测


为适应高速铁路无砟轨道结构层间损伤的快速检测，提出一种基于多元数据融合和深度学习的水泥沥青砂浆（CAM）间隙定位及损伤程度分类方案。基于垂直轴箱加速度（VABA）和垂直轮轨力（VWRF）数据，分析了边缘型和内部型层间间隙下车辆多动力响应数据的变化规律。设计改进的集合局部均值分解算法（IELMD），实现层间间隙下VABA和VWRF的数据去噪和弱损伤特征的初步增强预处理。测量和模拟的VABA和VWRF构成了用于层间间隙检测的多个动态响应数据集。构建了层间间隙检测模型：DTA-Tcnformer，将双时间卷积网络与注意机制、变压器架构和门控制机制集成在一起。将多个动态响应数据输入模型中，对两种层间间隙类型的 24 种情况进行定位和分类。分析了损坏情况和车速对定位效果的影响。所提出的模型对间隙特征的学习能力是可视化的。计算了间隙案例的误识别和漏识别，综合识别准确率为92.48%。对所提出模型的识别性能进行了比较和验证。