A Wasserstein generative adversarial network-based approach for real-time track irregularity estimation using vehicle dynamic responses

Abstract

Accurate and timely estimation of track irregularities is the foundation for predictive maintenance and high-fidelity dynamics simulation of the railway system. Therefore, it’s of great interest to devise a real-time track irregularity estimation method based on dynamic responses of the in-service train. In this paper, a Wasserstein generative adversarial network (WGAN)-based framework is developed to estimate the track irregularities using the vehicle’s axle box acceleration (ABA) signal. The proposed WGAN is composed of a generator architected by an encoder-decoder structure and a spectral normalised (SN) critic network. The generator is supposed to capture the correlation between ABA signal and track irregularities, and then estimate the irregularities with the measured ABA signal as input; while the critic is supposed to instruct the generator’s training by optimising the calculated Wasserstein distance. We combine supervised learning and adversarial learning in the network training process, where the estimation loss and adversarial loss are jointly optimised. Optimising the estimation loss is anticipated to estimate the long-wave track irregularities while optimising the adversarial loss accounts for the short-wave track irregularities. Two numerical cases, namely vertical and spatial vehicle-track coupled dynamics simulation, are implemented to validate the accuracy and reliability of the proposed method.

KEYWORDS:

• Track irregularity estimation
• Wasserstein generative adversarial network
• axle box acceleration
• vehicle-track coupled dynamics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant number 11790283, No. 51978587, No. 51778194), the Fund from State Key Laboratory of Traction Power (2019TPL-T16), and the 111 Project (grant number B16041), which is gratefully acknowledged by the authors.