Digital twin-driven wear prediction method for pantograph strip in urban rail vehicles

Aiming at the problems of time-consuming and labor-intensive manual inspection and low reliability of image recognition monitoring methods in the wear monitoring of pantograph strips for urban rail vehicles, taking the pantograph-catenary system of Chongqing Rail Transit Line 1 as the research object, a digital twin-driven wear prediction method for pantograph strips is proposed. Firstly, the overall framework of the digital twin system for pantograph strip wear prediction is constructed, elaborating the composition of the twin system, the interaction modes of each part, and the strip wear prediction process. Secondly, the rigid-flexible hybrid dynamics model of the urban rail vehicle pantograph-catenary coupling system is established by using the multi-body dynamics method and the finite element method to obtain the dynamic characteristics between the pantograph and catenary, and its surrogate model is established to achieve lightweight simulation. Furthermore, based on the adhesive wear theory and electrical contact theory, a wear rate calculation formula for pantograph strips was established. The parameter identification and calculation accuracy verification of the wear rate calculation formula were completed through current-carrying friction and wear simulation tests for the pantograph-catenary system. By coupling the dynamic surrogate model with the strip wear rate calculation formula, a pantograph strip wear profile prediction model considering the dynamic characteristics of the pantograph-catenary system was established. Finally, taking the pantograph strip on actual lines as a case study, the wear profile of the pantograph strip under actual operating conditions was predicted. According to the actual wear monitoring data of the pantograph strip, the result verification and parameter calibration of the wear prediction model was completed, providing theoretical guidance for the replacement and maintenance of pantograph strips in actual operations.