Research on Rail Transit Vehicle Wheel Axle Locking Fault Prediction Model

Abstract

Objective Improper assembly of wheel axles, excessive operation during maintenance leading to axle surface irregularities, or abrasions and defects caused by electrical corrosion during operation exacerbate abnormal faults such as wheel axle overheating and locking in rail transit vehicles. If left unattended, these faults may result in serious accidents. Therefore, it is necessary to study predictive methods and preventive measures for wheel axle locking faults in rail transit vehicles. Method Taking the occurrence of wheel axle locking faults in Nanjing Metro S7 Line trains as an example, analysis and mining of train monitoring data are conducted to explore their potential correlations and periodicities, providing a basis for train fault diagnosis. Based on traction mode, big data analysis is performed using real-time traction current, axle speeds, reference speed signals to establish a prediction model for wheel axle locking faults. Assessment of wheel axle locking phenomena caused by bearing seizure, increased internal resistance, and aging of the axle system is carried out to identify their health status and predict potential aging or seizure within the wheel axle. Result & Conclusion A prediction model for wheel axle locking faults in rail transit vehicles is established and validated through analysis of actual train operation data. However, due to limited sample data on wheel axle locking, subsequent online learning methods combining historical values with new data are required to improve the accuracy of the prediction model.

Keywords

• rail transit
• wheel axle locking
• big data analysis
• prediction model
• safe operation