Deep Learning for Monitoring the Health Condition of RailwayCrossings

Abstract

In this thesis, a method to monitor the health condition of railway crossings based on vibration data recorded by the accelerometers installed on the crossing is proposed. Due to various types of trains and other exogenous factors, responses obtained from accelerometers vary, even when the crossing has the same state condition. As a consequence, the degradation level is difficult to estimate. The method proposed in this paper uses Convolutional Neural Networks (CNN) algorithms for estimating the degradation level of the crossing and suppressing deviations caused by various inputs so that defects on the crossing can be detected in the early stage. The method is evaluated using a real-life dataset from a crossing located in Amsterdam. Different architectures are proposed and tested. With one of the architectures (ConvNet), the degradation level of the crossing can be estimated with minimum deviations (2.03 \% MSE). Other architectures performed in the range of 3.49 to 3.05 \% MSE. With the proposed methodology, defects on the crossing can be detected two months before the defects are spotted during the visual inspection.