Visual Collaborative Maintenance Method for Urban Rail Vehicle Bogie Based on Operation State Prediction

Abstract

The bogie is a crucial component of urban rail vehicles, and its performance plays a decisive role in the safe operation of vehicles. Aiming at the intelligent operation and maintenance requirements of rail transit equipment, in this paper, it takes several key parts of the urban rail vehicle bogie system as research objects, such as motor bearings, frames, fasteners, etc., and proposes a three-dimensional (3D) visual collaborative maintenance method. Firstly, a multi-sensor urban rail vehicle bogie running simulation experiment analysis platform was constructed, thereby establishing a database of running state and performance characteristics of the bogie in the whole life cycle. Then, the health status of key components of bogie was predicted by the state interval prediction model. Finally, the three-dimensional visual collaborative maintenance model proposed in this paper was integrated to realize the early warning of the bogie operation faults, 3D precise guidance of automatic location and maintenance operation information, and collaborative sharing of visual information among multiple users.

This work was supported in part by the Key Project of Research and Development Plan of Hunan Province under Grant 2018GK2044, in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ4084, in part by the National Natural Science Youth Fund Project under Grant 51805168, and in part by the Science and Technology Talent Project of Hunan Province -- Huxiang Youth Talent under Grant 2019RS2062.