Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies

Real-time detection of transport movement anomalies has attracted significant attention, since railway transport remains a key element of global logistics and passenger transportation. In the context of increasing traffic intensity, rising speeds and more stringent safety requirements, the identification of anomalies in the movement of rolling stock has become a critical task. Purpose: the selection of an algorithm for the positioning of rolling stock based on fuzzy logic (Takagi — Sugeno or Mamdani) is a justified choice in the event of a movement anomaly, as is the determination of the risk level of anomaly occurrence. Results: computerized scenario experiments have been conducted with the considered input data characterizing the main movement parameters (speed, vibration, acceleration, weather conditions, track condition) to ensure safety on railway sections. Discussion: the experimental results demonstrated the efficacy of the Takagi — Sugeno algorithm in determining the risk level of movement anomalies.