Open AccessResearch on Risk Prediction of Train Derailment based on Relative Wheel-rail Displacement
Author(s) -
Feifei Qin,
Dong Ming Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1965/1/012140
Subject(s) - derailment , train , acceleration , engineering , structural engineering , robustness (evolution) , automotive engineering , displacement (psychology) , track (disk drive) , mechanical engineering , psychology , biochemistry , chemistry , physics , cartography , classical mechanics , psychotherapist , gene , geography
Derailment safety is the basis for safe train operation, and the derailment coefficient is an important indicator for judging whether a train is derailed. Traditional derailment coefficient prediction methods have problems such as high cost, low prediction accuracy, and the inability to monitor the contact status between wheels and rails of running trains in real time. This paper proposes a train derailment coefficient prediction method based on image processing. Firstly, use the generative adversarial network to extract the edge curve of the wheel-rail contact area. Secondly, determine the wheel-rail lateral relative displacement based on the positional relationship between the rail and the centerline of the wheelset. Use the speed and acceleration sensors to obtain the train speed, lateral acceleration, and longitudinal acceleration, and finally use improvements. The BP neural network integrates wheel-rail lateral displacement, speed, lateral acceleration, and longitudinal acceleration data to obtain the train derailment coefficient at the next moment. The field test results show that the derailment coefficient prediction model in this paper can achieve accurate prediction of the derailment coefficient and has good robustness.