Developing Machine Learning-Based Models for Railway Inspection

Abstract

Smart railway maintenance is crucial to the safety and efficiency of railway operations. Successful deployment of technologies such as condition-based monitoring and predictive maintenance will enable railway companies to conduct proactive maintenance before defects and failures take place to improve operation safety and efficiency. In this paper, we first propose to develop a classification-based method to detect rail defects such as localized surface collapse, rail end batter, or rail components—such as joints, turning points, crossings, etc.—by using acceleration data. In order to improve the performance of the classification-based models and enhance their applicability in practice, we further propose a deep learning-based approach for the detection of rail joints or defects by deploying convolutional neural networks (CNN). CNN-based models can work directly with raw data to reduce the heavy preprocessing of feature engineering and directly detect joints located on either the left or the right rail. Two convolutional networks, ResNet and fully convolutional networks (FCN), are investigated and evaluated with the collected acceleration data. The experimental results show both deep neural networks obtain good performance, which demonstrate that the deep learning-based methods are effective for detecting rail joints or defects with the expected performance.

Keywords

• rail defects
• rail joint
• switch
• acceleration data
• wavelet
• machine learning algorithms