Response based track profile estimation using observable train models with numerical and experimental validations

Condition monitoring of railway tracks is essential in guaranteeing the running safety of railways. Track profiles are the primary source of external excitation for a train system. While Track Recording Vehicle is often utilized for maintenance purposes, this particular vehicle is expensive and difficult to use for small railway operators. Therefore, track profile estimation through in-service vehicle response measurements, which potentially provides efficient and frequent measurement, has been studied. However, the quantitative evaluation of the vertical and lateral track profile irregularities is still challenging as the inverse analysis solutions are sometimes inaccurate and even unstable. In this paper, numerical analyses are first carried out to evaluate track profiles from acceleration and angular velocity responses measured on a train car body. For the inverse analysis, an Augmented State Kalman Filter is utilized to solve the problem using 4 degrees of freedom observable train models. The sensor installation locations are investigated through observability rank condition analysis with different measurement layout. Secondly, a field experiment is carried out in a local Japanese in-service railway network to estimate track profile from car body motions. Smartphones are utilized for the field test measurements as prevalent sensing devices. The effectiveness of the proposed approach is demonstrated with the observable train model. Numerical analyses and field experiments clarify the proposed track profile estimation's capability using only one on-board sensing device.

Automated summary

Condition monitoring of railway tracks is crucial for ensuring the running safety of railways. This study evaluates track profiles through numerical analyses and field experiments, demonstrating the capability of track profile estimation using on-board sensing devices such as smartphones.