With development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data for the data-driven method, in which the wagon was regarded as a 47-degree-of-freedom multi-body subsystem, and the track was treated as a multi-layer discrete-elastic support beam subsystem with absent sleeper support. Then, an architectural hierarchy of a three-layer convolutional neural network (TLCNN) was developed, which includes three convolutional layers and two pooling layers, and a method for reconstructing one-dimensional sleeper vertical displacement to a two-dimensional time–space matrix was also proposed. Thirdly, verification was carried out by comparing the simulation and experimental results to illustrate the accuracy and reliability of the mathematical model, and the dynamic behaviour of the track with absent sleeper support was investigated. Lastly, the established TLCNN was used to train the raw data of the sleeper vertical displacement and detect the existence of absent sleeper support. Results show that the integration of model-based and data-driven methods was a reliable and effective approach for the detection of absent sleeper support. The proposed TLCNN can acquire and extract robust characteristics in a noisy environment. To handle more complex recognition tasks and further improve performance, deeper CNN models and larger sample sizes should be preferentially considered in practical applications.

随着重载铁路的发展，增加的轴重和牵引重量给铁路轨道的使用性能和安全维护带来了巨大的挑战。使用大数据对混凝土枕木和道碴进行缺陷识别至关重要。本文重点关注有砟轨道中轨枕支撑缺失的检测，重点是基于模型和数据驱动方法的集成。为此，首先建立了由货车、轨道和轮轨接触子系统组成的数学模型，以获取数据驱动方法所需的原始数据，其中货车被视为47个自由度多体子系统，轨道被视为没有轨枕支撑的多层离散弹性支撑梁子系统。然后，开发了三层卷积神经网络 (TLCNN) 的体系结构层次结构，其中包括三个卷积层和两个池化层，并提出了一种将一维轨枕垂直位移重构为二维时空矩阵的方法. 再次，通过对比仿真和实验结果进行验证，说明数学模型的准确性和可靠性，并研究了无枕木支撑轨道的动力特性。最后，利用建立的TLCNN对枕木垂直位移的原始数据进行训练，检测是否存在枕木支撑缺失。结果表明，基于模型和数据驱动的方法的集成是检测缺少枕木支撑的可靠且有效的方法。所提出的 TLCNN 可以在嘈杂的环境中获取和提取稳健的特征。为了处理更复杂的识别任务并进一步提高性能，在实际应用中应优先考虑更深的CNN模型和更大的样本量。