Neural network method for inversion of hard point height of medium and low speed maglev track

Abstract

The maglev power supply system is realized by the loop formed by the contact rail. Hard points on the contact rail are the key factors that affect the continuity of power supply, and even affect the safe operation of the track in serious cases. The hard points height characteristic are related to the separation time and distance between the collector shoe and the contact rail. In order to explore the characteristics of the height data, a hard-points platform for simulating the contact rail is built. The characteristics of the acceleration signal passing through the simulated hard points platform are extracted by time-frequency analysis. Using the neural network model to explore the correlation, the regression prediction of the contact rail height value can be realized. Based on this method, the prediction error of simulating hard points inversion at a specific height is within 2%, and the effect is good. At present, it has been used in engineering practice, and it has played an important role in the detection and maintenance of the hard points of the medium and low speed maglev contact rail.