Open AccessResearch on Dynamic Perception Algorithm for High Speed Maglev Track Irregularity
Author(s) -
Sansan Ding,
Fujie Jiang,
Xiaofeng Sun,
Jingyu Huang,
Dongshuai Li,
Huibai Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/455/1/012129
Subject(s) - maglev , track (disk drive) , artificial neural network , acceleration , vibration , computer science , train , stability (learning theory) , algorithm , control theory (sociology) , automotive engineering , simulation , engineering , artificial intelligence , acoustics , physics , electrical engineering , machine learning , cartography , control (management) , classical mechanics , geography , operating system
The high-speed maglev track irregularity is an important factor affecting the safety and stability of high-speed maglev trains. Based on the basic principle of high-speed maglev vehicle orbit, the deep neural network is used to establish the relationship between the vibration acceleration of the vehicle body and the track irregularity, so as to realize the real vehicle detection of the track irregularity. The results show that the relative error of the prediction of the track irregularity by the deep neural network is 6.04%, which basically meets the actual measurement requirements in the project. At the same time, it is demonstrated that the model with 8 input nodes has higher prediction accuracy and the high-speed maglev track is not smooth. Measurement provides theoretical support.