Fault Detection in Railway Switches using Deformable Convolutional Neural Networks

Abstract

Recently, time series classification methods based on Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance outperforming former ensemble-based methods like HIVE-COTE on a multitude of time series datasets. Inspired by the current rise of Deep Neural Networks (DNNs) end-to-end classifiers for time series classification, we propose utilisation of Deformable Convolutional Neural Networks (Deformable CNNs), which have already proven to drastically enhance classification performance on image classification tasks. Our aim is to evaluate the applicability of such methods on the practical use-case of a German railway provider, in which sensory data from railway switches is employed to detect and classify faults in switching operation. Prior to any classification, we have to address two main issues, which is that the available data is in a raw, unlabelled format and the contained time series have vastly varying length. We cope by applying extensive pre-processing and semi-supervised labelling. As baseline classifier, we use a conventional KNN classifier that is tailored to enable handling of sensory data. Finally, we compare the baseline classifier against more advanced DNN classifiers and discuss their feasibility in general and in context of our use-case.