Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network

Condition monitoring of railway tracks is effective for the sake of an increase in the safety of regional railways. This study proposes a new method for automatically classifying the type and degradation level of track fault using a convolutional neural network (CNN), which is a machine learning method, by imaging car body acceleration on a time-frequency plane by continuous wavelet transform. As a result of applying this method to the data measured in regional railways, it was possible to classify and extract the sections that need repair according to the degree of deterioration of the tracks, and to identify the track fault in those sections.