Risk Identification Method for High-Speed Railway Track Based on Track Quality Index and Time-Optimal Degree

Abstract

The real-time identification and early warning of the state of the track are significant to keep the high-speed railway (HSR) safe, stable, and comfortable. This paper proposes a new risk identification method based on the time-ordered weighted averaging operator and track quality index (TOWA-TQI) by using the time-weighted vector to aggregate time-optimal information of objects. Meanwhile, an energy coefficient is introduced to make the weighted track irregularity data as the same energy as the original track irregularity data that can control the weighted amplitude at the same level. To quickly classify the objects, this paper proposes a center and radius clustering (C-R clustering) method that can classify the points into different categories by judging that the distance from the central point is less than the corresponding radius. Moreover, the specific location is located by labeling the categories. Lastly, a practical case is carried out to verify that the proposed method is more accurate and effective.