## 1 Introduction  
The wheel-rail adhesion state is a critical factor determining the traction and braking performance of heavy-haul trains. The dynamic variations in the adhesion coefficient, primarily influenced by third-body media (e.g., water, oil) on the rail surface, directly impact operational safety and control precision [1, 2, 3, 4, 5]. Therefore, accurately identifying the rail surface condition and transmitting this information to the train in real time is essential for enabling timely control system responses and ensuring high-performance train operation. The region near the maximum adhesion coefficient represents the optimal utilization of traction power, making it a focal point in adhesion control research. Current methods for estimating the maximum adhesion coefficient exhibit significant limitations. The adhesion slope method [6, 7, 8] is constrained by a narrow range of model observation frequency points, while the creep-slip velocity method [9] faces challenges in accurately obtaining the creep-slip velocity, limiting its practical applications. Althoughfuzzy control mitigates model dependency, its reliance on one-dimensional dynamic inputs (e.g., creep-slip velocity, adhesion coefficient) results in insufficient confidence and accuracy in complex operating conditions [10]. Integrating multi-source sensing information, particularly real-time visual identification of the rail surface condition, can enhance both the accuracy and reliability of maximum adhesion coefficient estimation while improving environmental adaptability. In recent years, deep learning-based visual identification of rail surface conditions has achieved promising results. While high-precision classification models, such as ResNet and VGG, have demonstrated strong performance, their high computational complexity (e.g., ResNet-18 contains 11.7M parameters) limits their applicability to real-time, embedded in-vehicle platforms [11]. Lightweight models [12; 13; 14] and model compression techniques [15; 16; 17; 18] can reduce computational overhead but often suffer from accuracy degradation when trained on small datasets. Therefore, achieving high accuracy, low latency, and strong generalizability in rail surface condition identification remains a critical challenge. Through the aforementioned analysis, this study aims to address the engineering challenges faced by existing rail surface state identification models in terms of accuracy, computational complexity, and feasibility for onboard deployment. Simultaneously, it seeks to enhance the confidence and accuracy of maximum adhesion coefficient estimation by incorporating rail surface state information. The key contributions of this study are as follows:  
1. A lightweight rail surface condition identification method is proposed to achieve a balance between model accuracy and real-time performance. Specifically, a multi-condition rail surface state image dataset is constructed, and a \"teacher-student\" co-optimization framework is established. By leveraging knowledge distillation, the multi-scale feature extraction capability of the teacher model is effectively transferred to the student model, thereby maintaining high recognition accuracy under lightweight constraints. Furthermore, a mixed-precision training framework is embedded to enhance computational efficiency, thereby meeting the real-time requirements of the onboard system. 2. A multi-source information fusion mechanism is designed to integrate rail surface condition information into the maximum adhesion coefficient estimation system. This study adopts a fuzzy control-based approach to achieve multi-source information fusion, where the application of fuzzy logic significantly mitigates the impact of information uncertainty on adhesion coefficient estimation. Consequently, the accuracy of maximum adhesion coefficient estimation in complex environments is effectively improved.