Estimation of turnout irregularities using vehicle responses with improved BiLSTM and Gaussian process regression

Abstract

Railway turnout irregularities pose a safety risk, but existing detection methods using expensive trains with inertial navigation systems and laser measurement devices are cost-prohibitive and time-consuming for high-speed railways. This paper proposes a low-cost and real-time solution for estimating railway turnout irregularities using vehicle body acceleration, allowing for continuous monitoring of dynamic changes. A Bayesian-optimized improved bidirectional long short-term memory neural network model (BO-BiLSTM) was proposed in this paper, utilizing the vehicle's vertical and lateral acceleration as input to achieve point estimation of irregularities, with an estimation error approximately 50% lower than that of traditional recurrent neural networks. In addition, an interval estimation model combining BO-BiLSTM and Gaussian process regression (BO-BiLSTM-GPR) was proposed. Compared to the traditional interval estimation method Bootstrap, under the condition of narrow interval width, GPR ensures that over 90% of the measured values fall within the estimated interval and provides a better solution to the trade-off between interval estimation reliability and uncertainty.