Numerical Investigation of Unsupported Sleeper Detection Method Based on LSTM and Monte Carlo Dropout

Abstract

Track management is important to improve the safety and ride comfort of trains. The unsupported sleeper, which is one of the track anomalies, is currently difficult to be detected by conventional track inspection methods, because of its short wavelength components and appearance only under train passage. This study proposed the method for detecting the unsupported sleeper from vehicular vibration responses, based on highly nonlinear time series prediction by LSTM and the estimation of uncertainty by Monte Carlo Dropout. A multibody dynamic model of the express train was utilized, and the effects of the unsupported sleepers on the dynamic performance of the train were studied. The proposed method was then applied to the simulation data and its performance was evaluated. It was confirmed that the the proposed method improved the detection ability comparing with conventional methods, while obtained high accurate detection for the cases with two or more unsupported sleepers.