Slab arching degree identification and evaluation based on track dynamic inspection data

Abstract

Due to structural deterioration and the external environment, the ballastless track is prone to slab arching during high-temperature periods. Arching will weaken the constraints between components, affect the smoothness of the line, and even endanger train safety in severe cases. Therefore, it is necessary to identify the arching in time and clarify the arching severity, which is of great significance for scientifically formulating maintenance plans and ensuring line stability. The existing research has established track geometry-based methods for arching identification, but they are difficult to recognize small arching and cannot quantitatively evaluate arching degree. To this end, this paper proposed a set of arching identification and evaluation methodology based on track dynamic inspection data. Combining the simulated and measured data, the time–frequency characteristics of car body vertical vibration caused by arching were finely extracted, and the arching-induced vibration index was established for identification. Then, a deep learning hybrid model for arching degree evaluation was proposed based on the analysis of the relationship between multiple feature indexes and arching gap height. The results showed that car body vibration could identify the arching defects with gap heights exceeding 1 mm, and the arching degree could be evaluated quantitatively based on multiple feature indexes. In practice, the proposed method can be used to detect arching of different degrees, assist maintenance decision-making, and help avoid the occurrence of serious failures.