# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 1 Introduction
When the operating conditions change, the friction coefficient also changes, and, due to the complex evolution and mechanisms of pantograph-catenary wear, these models cannot accurately represent the wear of the pantograph-catenary system, making it difficult to predict wear precisely using any single method. In mechanical friction systems, wear is often analyzed from the perspective of energy dissipation. [PERSON] et al. [14] found that, under dry friction, the wear rate of graphite exhibits a significant linear relationship with the frictional dissipation power of the mating surfaces. [PERSON] developed a mathematical model that relates the wear volume of materials in sliding contact to the dissipated energy, which can be used to predict the component's service life [15]. Additionally, studies [16; 17; 18; 19] have shown that surface friction wear is related to its dissipated energy. By investigating the relationship between friction wear and dissipated energy, the wear mechanism and related energy dissipation can be analyzed, linking dissipated energy to the wear volume and proposing a wear calculation method based on energy dissipation theory. Although the energy dissipation method has been used to study material wear in mechanical friction systems, there is still limited research in the field of sliding electrical contact, particularly in the pantograph-catenary system. No studies have been reported on the prediction of pantograph slide wear from the perspective of energy dissipation in the pantograph-catenary system. In the interdisciplinary research of AI-assisted predictive modeling and thermoelectric systems, remarkable progress has been achieved in recent years. [PERSON] et al. [20] proposed a physics-guided neural network (PGNN), integrating wear mechanism knowledge with data-driven learning to provide a novel paradigm for high-precision and interpretable wear prediction in pantograph-catenary systems. [PERSON] et al. [21] developed a multi-agent reinforcement learning (MARL) algorithm based on the Nash equilibrium framework, enabling heterogeneous agents to collaborate under global value function guidance for proactive pantograph control and enhanced system stability. [PERSON] et al. [22] constructed a \"data-driven-model prediction-experimental validation\" closed-loop framework, integrating material descriptors such as matrix encoding and radial distribution analysis to achieve >90% accuracy in predicting the thermoelectric figure of merit (ZT) and accelerate the development of novel thermoelectric materials. These studies have advanced interdisciplinary applications of AI in thermoelectric systems from the perspectives of modeling, control, and material design. This paper presents a novel method based on energy dissipation to predict the wear of the pantograph slide and explain its wear mechanism. The wear of the pantograph slide is quantitatively characterized by the current-carrying frictional energy dissipation in the pantograph-catenary system. Based on the significant linear relationship between the wear of the pantograph slide and the current-carrying frictional energy dissipation, a basic mathematical model for pantograph slide wear based on energy dissipation is established. The variation in the current-carrying frictional energy dissipation with the operational parameters is studied, providing a quantitative explanation of the interaction between electrical wear and mechanical wear from an energy perspective. This method offers a new approach and insights for the lifetime prediction of the pantograph-catenary friction pair and the study of current-carrying wear mechanisms.