Intelligent Analysis System for Turnout Friction Current Based on Density Clustering Algorithm and Breadth-first Search Algorithm

Abstract

Objective The turnout friction current on-site testing and adjustment involve three major unfavorable aspects of complex processes and high risks, high professional requirements for maintenance personnel, and significant subjective component in determining numerical values, calling for enhancement of the intelligent operation and maintenance level of turnout based on various intelligent algorithms and technologies. Method The characteristics of the four stages of turnout friction current testing curve are analyzed, and an intelligent analysis system for turnout friction current is proposed. The functionality and working principle of the system are elucidated, and standard values and threshold ranges for friction current are established. Based on density clustering algorithm and breadth-first search algorithm, the system can automatically retrieve the turnout friction current value. Screenshots of the system debugging interface are presented to illustrate how the system provides operational suggestions for on-site maintenance personnel in obtaining turnout frictional current values. Result & Conclusion This intelligent system demonstrates excellent usability and achieves the predefined goals of saving maintenance time, reducing maintenance costs, and increasing maintenance efficiency.

Keywords

• urban rail transit
• signal
• intelligent operation and maintenance
• turnout switch machine
• friction current
• density clustering algorithm
• breadth-first search algorithm