# Developing Machine Learning-Based Models for Railway Inspection
## 7 Conclusions  
This paper presented machine learning-based methods for railway inspection, including a feature-based method and a deep neural network-based method using acceleration data. The deep learning-based method is an end-to-end time series classification approach for the detection of rail joints on railway track by training convolutional neural networks. It enhances the classification-based detection method with applicability and easy-deployment features. The advantages of the deep neural network-based approach are: (1) a significant reduction in the heavy data preprocessing for feature extraction; and (2) ability to detect joints on left or right rail using one global model. Our approach overcomes the interference issue with acceleration data, i.e., a discontinuity (joint or defect) on one side of rail also generates high amplitude of the acceleration signal on the other side  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**ResNet**} \\ \cline{3-5}  & & **Recall** & **Precision** & **F-Measure** \\ \hline \multirow{2}{*}{Data\_1} & Left (10 joints) & 0.83 \(\pm\) 0.07 & 0.83 \(\pm\) 0.08 & 0.83 \(\pm\) 0.06 \\ \cline{2-5}  & Right (9 joints) & 0.94 \(\pm\) 0.06 & 0.89 \(\pm\) 0.06 & 0.91 \(\pm\) 0.08 \\ \hline \multirow{2}{*}{Data\_2} & Left (134 joints) & 0.98 \(\pm\) 0.04 & 0.77 \(\pm\) 0.03 & 0.85 \(\pm\) 0.04 \\ \cline{2-5}  & Right (142 joints) & 0.89 \(\pm\) 0.03 & 0.87 \(\pm\) 0.02 & 0.88 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{Data\_1} & \multicolumn{3}{c}{**FCN**} \\ \cline{3-5}  & \multicolumn{3}{c}{**Recall**} & **Precision** & **F-Measure** \\ \hline \multirow{2}{*}{Data\_2} & Left (10 joints) & 0.82 \(\pm\) 0.12 & 0.71 \(\pm\) 0.13 & 0.74 \(\pm\) 0.15 \\ \cline{2-5}  & Right (9 joints) & 0.96 \(\pm\) 0.08 & 0.81 \(\pm\) 0.13 & 0.86 \(\pm\) 0.13 \\ \hline \multirow{2}{*}{Data\_2} & Left (134 joints) & 0.98 \(\pm\) 0.03 & 0.76 \(\pm\) 0.06 & 0.87 \(\pm\) 0.05 \\ \cline{2-5}  & Right (142 joints) & 0.95 \(\pm\) 0.04 & 0.83 \(\pm\) 0.05 & 0.89 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of deep learning-based models.  
of the rail. This interference effect can increase the false alarm rate if a separate model for each side of the rail is used. With a unified model trained by data collected on both sides, this effect is learned in the model such that the model is able to distinguish joints on left or right rail specifically. The experimental results demonstrated that ResNet and FCN both achieved good performance in joint detection.  
Although this study was focused on detecting rail joints, we believe that the developed approach can be easily extended to other conditions on rail surface. The next step of the research is to apply the approach to other rail surface defects [33]--such as localized surface collapse (LSC), rail end batter (REB), or crushed head (CH)--prior to rail failure. These defects have been identified by a recent derailment investigation as leading indicators for rail failure and derailment.  
The contribution for each author is as follows. Conceptualization: Y.L., C.Y. and Y.S.; Methodologies: C.Y. and Y.L. are the major contributors to the feature-based classification method; Y.S. and Y.L. are the major contributors to the deep-learning based method; Validation: C.Y., Y.S., C.L. and Y.L.; Data curation: Y.L.; Writing--original draft preparation: C.Y. and Y.S.; Writing--review and editing: Y.L. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We are grateful to Transport Canada for providing a rail inspection car to collect rail joint data. We appreciate the suggestions and valuable discussion from our colleagues and the coop students for helping with the experiments.  
The authors declare no conflict of interest.