Wheel Wear Modeling and Re-Profiling Strategy Optimization Based on the Maintenance Data of a Three-Axle Bogie Locomotive

Abstract

Wear caused by wheel–rail contact forces is inevitable during vehicle operation, which has an important impact on the security and stability of train operation. Therefore, it is of great significance to study wheel wear patterns and optimize re-profiling strategies to extend service life. Based on the wheel wear data of three-axle bogie locomotives, this paper proposes a data-driven hybrid wheel wear model and optimization schemes of the re-profiling strategy. The wear model consists of a wheel flange thickness wear model, a wheel diameter wear model, and a re-profiling ratio coefficient model. Then, utilizing the above models, the optimization of the re-profiling strategy for different axle position wheels is raised, and the optimization of the complete vehicle re-profiling strategy is presented by considering the wheel diameter difference. Finally, a wheel data-driven analysis platform was developed to enable the management and utilization of wheel maintenance data. Analysis of extensive maintenance data indicates that the guide wheels wear the fastest, approximately 22.2% higher than the middle wheels, which wear the slowest. The re-profiling ratio coefficient model indicates that the ratio coefficient increases as the wheel flange thickness before re-profiling increases. Simulations demonstrate a longer expected wheel life with a flange thickness between 28 and 32.5 mm. Compared to measured values, the optimization strategy reduces complete vehicle re-profiling by 21.5%. Through the initial implementation at CRRC Dalian Locomotive Ltd, it has become evident that this methodology offers a viable solution to enhance the service longevity of locomotive wheels.