第１部門④

[21006-11-01] Track profile estimation using a half car model and unsupported sleeper detection considering train vehicle weight difference (Proceedings of Symposium on Applied Mechanics)

*Daichi Masuda¹, Masaru Kitahara¹, Tomonori Nagayama¹, Xue Kai¹, Di Su¹ (1. The University of Tokyo)

Keywords:track profile estimation, unsupported sleeper, half car model, Kalman filter, system identification

This study presents an efficient approach to estimate the track profile and the unsupported sleeper using train vehicle responses. A half car model is used and an extended state-space model is defined that includes the track profiles in the state vector. Kalman filter and RTS smoothing are then employed to estimate the track profiles. The half car model parameters can also be calibrated by solving an optimization problem based on constraints on the estimated track profiles. Furthermore, the unsupported sleeper is detected using a statistical index as a large difference between the track profiles estimated using vehicles with different weights.