## 2 General Structure of the Methodology
### Multi-Source Data Input Module  
This module facilitates \"vision-dynamics\" bimodal data acquisition, integrating real-time data collection and processing through an industrial camera and the constructed train traction dynamics model. It provides the necessary inputs for subsequent rail surface condition identification and maximum adhesion coefficient estimation.  
Visual modality. To capture image data of the rail surface conditions, a dedicated data acquisition platform is developed, employing industrial cameras to capture rail surface im  
Figure 1: General architecture of the methodology.  
ages under dynamic conditions. These images encompass three typical operational states: dry, wet, and oily. The image data undergo a series of pre-processing steps, including grayscaling, denoising, enhancement, perspective transformation, rail surface area extraction, and data augmentation. These procedures aim to enhance micro-textural features, augment the training dataset, and fulfill the input requirements for the subsequent neural network model.  
Dynamic modes. Drawing on the traction dynamics model of the HXD1 locomotive, this modality emphasizes the mathematical representation of the wheel-rail interaction and adhesion force, with particular attention to the influence of creep-slip velocity on the adhesion force. A full-dimensional state observer is incorporated to estimate the real-time adhesion coefficient between the wheels and rails, integrating the kinetic model to reflect the adhesion state. The real-time creep-slip velocity and the estimated adhesion coefficient serve as crucial data inputs for the subsequent estimation of the maximum adhesion coefficient.  
Based on the preceding analysis, it is evident that rail surface condition identification must simultaneously address the demands of high accuracy, low latency, and strong generalization. To enhance model inference speed while maintaining high identification accuracy, this study proposes a lightweight identification approach that leverages knowledge distillation and transfer learning techniques, as illustrated in Figure 2. GoogLeNet is selected as the teacher model due to the well-established advantages of its Inception module in multi-scale feature extraction [19]. Compared with ResNet, which is widely adopted in existing studies on rail surface state recognition, GoogLeNet significantly reduces the number of parameters while having a deeper network structure. This allows it to achieve a better balance between computational efficiency and accuracy, making it well suited for the task of this study. MobileNet is chosen as the student model due to its depthwise separable convolution structure, which demonstrates superior performance in resource-constrained environments [13]. Compared with other classical lightweight models, such as EfficientNet and ShuffleNet, MobileNet offers a moderate number of parameters and a shallower network depth, thereby achieving a better trade-off between accuracy and inference speed. Through knowledge distillation, the teacher model's knowledge is efficiently transferred to the student model, thereby improving inference efficiency without compromising accuracy. Furthermore, to further enhance the model's generalization capability, transfer learning is employed, utilizing pre-trained ImageNet weights for the rail surface condition identification task, which also significantly reduces model training time. During the training phase, a mixed-precision training strategy is incorporated to optimize computational efficiency. By combining these techniques, a lightweight rail surface condition identification model is developed, achieving both high efficiency and reliability.