Research on Health Management and Early Warning System of Railway Freight Locomotive

Abstract

The abnormal of railway freight locomotives may directly lead to the outage of locomotives. The current maintenance system cannot meet the reliability requirements of freight locomotives. For this season, it is necessary to establish a railway freight locomotive health management and fault early warning system. By analyzing the current development of freight locomotives, this paper analyzes the requirements of fault prediction and health management system of freight locomotives, uses machine learning algorithm to model and analyze, evaluates the health status of each equipment and system, comprehensively considers the maintenance cost and system reliability, transforms the experience maintenance to the state maintenance, and optimizes the maintenance strategy. Taking the axle temperature system as an example, the application of health management and fault early warning system can effectively improve the reliability of freight locomotive operation and reduce maintenance cost.