Research on Dynamic Detection and Predictive System of Grooved Rail of Tram Twist Irregularity

In recent years, the use of modern trams, mainly in the form of grooved track, has seen a rapid growth in China. However, most of the current detection methods for grooved rail of tram twist irregularity use manual detection, which has low detection precision and low working efficiency. In addition, there is no predictive for grooved rail of tram twist irregularity. Twist irregularity, as one of the important data of the grooved rail geometric parameters, has a great impact on the safety, reliability and stability of train operation. Therefore, this paper proposes a method to collect the grooved rail data, using the existing algorithm to calculate the twist irregularity value, and using the gray system prediction combined with the error correction algorithm to predict and analyze the grooved rail twist irregularity. The experimental results show that the detection and prediction values have high accuracy and good stability. It provides a method for the dynamic detection and prediction of the grooved rail of tram twist irregularity.