Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Abstract

Purpose – The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models. Design/methodology/approach – The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle. Based on data mechanism models, it predicts the lifespan of key components, evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle. Findings – The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system, which helps operators to monitor the operation of vehicle online, predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle. Originality/value – This system improves the efficiency of rail vehicle operation, scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

Keywords

• Rail transit
• Rail vehicle
• Expert diagnosis
• Intelligent operation and maintenance
• Deep learning
• Lifespan prediction