Affiliation:
1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
2. College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China
Abstract
Any system for evaluating the safety service performance of heavy-haul railway lines must effectively reflect the real-time service status of the line. The working conditions of heavy-load lines are complex and diverse, particularly on uphill sections. Existing evaluation systems struggle to accurately reflect the service conditions of long and steep uphill sections bearing heavy loads, posing a significant threat to the safe operation of these lines. To address this problem, we propose a new method for evaluating the safety service performance of long and steep uphill sections of heavy-haul railway lines by establishing a scoring system based on the Analytic Hierarchy Process (AHP). First, damage indicators for heavy-haul lines are categorized into three groups: track geometry status indicators, track structure status indicators, and track traffic status indicators. Using data from existing heavy-haul lines and maintenance experiences, we determine a score deduction standard, classifying lines into four levels based on their safety service quality. Next, we establish a coefficient table for the service performance of long and steep uphill sections after the corresponding scores are deducted. Using data for the length and elevation grade of the actual uphill section, we adjust the deducted scores of the track structure status indicators, enhancing the evaluation system’s accuracy in describing the working conditions. Finally, we verify the stability of the entire system by conducting a sensitivity analysis of the indicator evaluation results using the One-At-a-Time (OAT) method. This method fills a critical gap in the safe operation and maintenance of heavy-haul railways and provides a safety guarantee for the operation of long uphill sections of heavy-haul railways.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Reference14 articles.
1. Advancing railway track health monitoring: Integrating GPR, InSAR and machine learning for enhanced asset management;Koohmishi;Autom. Constr.,2024
2. Xiong, L., Jing, G., Wang, J., Liu, X., and Zhang, Y. (2023). Detection of rail defects using NDT methods. Sensors, 23.
3. Railway track online detection based on optical fiber distributed large-range acoustic sensing;Xie;IEEE Internet Things J.,2023
4. Predicting the Remaining Useful Life of Rails Based on Improved Deep Spiking Residual Neural Network;He;Process Saf. Environ. Prot.,2024
5. Research on the Service Life of Steel Rails in Shuohuang Heavy Load Railway;Shang;Railw. Archit.,2022