# 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 battery, 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. rail defects; rail joint; switch; acceleration data; wavelet; machine learning algorithms; deep neural network; CNN +
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real-time and advises the staff when the impact of a given wheel is too high. In the North American railway network, a set of threshold values has been developed and implemented to flag the bad actor wheels since 2004. Building on WILD techniques, we have developed the WILD Predictor [8; 9; 10] to predict wheel failures before they reach these threshold values. The WILD predictor can predict wheel flats, out of round wheels, and estimate the time for wheels to reach the impact force thresholds using the machine learning-based predictive models. There are many available techniques developed by the railway industry and community to detect rail surface defects [6; 11; 12; 13; 14]. One cost-effective method of detecting rail surface defects such as shelling, squats, split heads, engine burns, etc. [15] is to use accelerometers mounted to the bogie side frames or wheel axles. The collected acceleration data from the sensors were processed with a pre-determined threshold value to judge the rail surface defects. The technique is simple, useful, and applicable for detecting rail joints or broken rails. However, much more work remains to be done in order to distinguish the joint (or broken rail) from various track surface defects since these surface defects generate high amplitude vertical accelerations in the axles and side frames as well. Due to such limitations, the existing threshold-based algorithms usually require additional visual inspection of these areas of track, which greatly increase the cost and complexity of the techniques. Therefore, more intelligent algorithms are needed so that the accelerometers can automatically distinguish the rail defects from rail joint (or broken rail) and other special track features with discontinuities. As the first step to distinguish rail surface defects from other track features, the present work is focused on the detection of rail joints on both sides of the track. To this end, we first developed machine learning methods to build the models to detect the rail joints using the accelerating data collected from an inspection vehicle [16]. The developed models can detect the rail joints with high accuracy. However, this method has two weaknesses: (1) the model requires the feature data from raw data and (2) each side of the track needs a separate model for detection. This creates difficulties to deploy or apply the models in railway operations. To address these limitations, we propose to develop deep learning-based models by applying convolutional neural networks (CNN).