## 4 Conclusions  
Aiming at the engineering problems of a large number of parameters, high computational delay, and difficulty in realizing onboard deployment of the current rail surface state identification model, this paper proposes a lightweight rail surface state identification method for estimating the maximum adhesion coefficient of the wheel-rail, which can improve the traction and braking efficiency of the train by accurately identifying the rail surface state in front of the rail, integrating this state information into the framework of estimating the maximum coefficient of the wheel-rail, and then providing the threshold value of the maximum coefficient of adhesion for the optimal adhesion control. The conclusions are as follows:  
1. The lightweight rail surface condition identification model proposed in this paper, based on knowledge distillation and transfer learning, effectively meets the practical requirements for high accuracy, low latency, and strong generalization in rail surface state recognition tasks. By employing GoogLeNet as the teacher model and MobileNet as the student model, the domain adaptation capability is enhanced through transfer learning. Additionally, the knowledge distillation strategy allows the student model to inherit knowledge from the teacher model, thereby reducing the number of parameters while maintaining accuracy. Furthermore, the introduction of mixed-precision training during the training process further reduces inference latency. The experimental results demonstrate that the proposed model achieves a size of only 4.21 MB, a test set accuracy of 97.38%, and an inference time of 0.0371 s, showcasing excellent overall performance and meeting the expected objectives. 2. The multi-source information fusion strategy designed in this paper can realize the fusion of rail surface condition information and maximum adhesion coefficient estimation. Through the design of a reasonable fuzzy control strategy, the train operation information is fuzzified and then compared with the set standard rail surface curve, and the similarity with the standard rail surface is obtained after anti-fuzzification. On this basis, the similarity is corrected by incorporating the rail surface condition information, and the corrected similarity is weighted as a weighting coefficient with the maximum adhesion coefficient value of the standard rail surface, and the maximum adhesion coefficient available for the current rail surface is calculated. The experimental results prove that the method can improve the estimation accuracy under arbitrary rail conditions, and the estimation error of a dry simulation track is reduced from 12.6% to 9.8%, the estimation error of a wet simulation track is reduced from 6.7% to 4.1%, the estimation error of a rainy and snowy simulation track is reduced from 4.1% to 2.7%, and the estimation error of an oily simulation track is reduced from 23.1% to 12.0%, which can provide a more accurate and precise maximum adhesion coefficient for optimal adhesion control and a more accurate threshold value for the maximum adhesion coefficient. Although this study has achieved preliminary results, there is still much room for improvement. The rail surface condition information obtained in this paper should be the environmental information in front of the train operation; it takes some time for the wheel-rail to run to the front, and the data transmission will also lead to time delay. Therefore, there exists an unsynchronized information between the recognized rail surface condition and the environment where the train is actually located. Although the time is very short, the problem of spatial and temporal synchronization still needs to be taken into account; the currently used rail surface condition dataset lacks the categories of icy and snowy rail surfaces, water and oil mixed rail surfaces, and wet leafy rail surfaces, which need to be further expanded to make the algorithm more extensive.