## 4 Conclusions and Future Work  
At present, rails and fasteners are still inspected with the aid of automated visual inspection and manual inspection, despite the fact these methods require huge investment in both capital and time. Moreover, automated visual inspection becomes a challenge when the rail and its component are obscured due to adverse environmental conditions, such as snow, debris etc. The authors proposed a train-based differential eddy current sensor that can overcome such challenges in the previous study [24]. This paper attempts to develop an intelligent system with the aid of machine learning approach to facilitate, effective and reliable health monitoring of track system by reducing human biases and error for detection of the state of railway fastening system. The data set used for this study was obtained from the measurements carried out along a heavy haul line in the north of Sweden using the differential eddy current measurement system. Three sets of data were used for analysis in this study, two of which were obtained from the individual channel of the sensor system (18 kHz and 27 kHz), four features representing each. The third data set comprised of the combined features from both channels (eight features). Six machine-learning algorithms were selected and compared among themselves for all three scenarios of data, to determine the best-performing model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**F1-Macro**} & \multicolumn{3}{c}{**Macro Precision**} & \multicolumn{3}{c}{**Macro Recall**} \\ \cline{2-10}  & **18 kHz** & **27 kHz** & **18 + 27 kHz** & **18 kHz** & **27 kHz** & **18 + 27 kHz** & **18 kHz** & **27 kHz** & **18 + 27 kHz** \\ \hline GNB & 86.06 & 86.24 & 89.02 & 83.09 & 82.6 & 84.13 & 89.24 & 90.21 & 94.69 \\ SVM & 91.19 & 92.92 & 93.15 & 90.64 & 91.72 & 92.16 & 92.66 & 94.15 & 94.29 \\ K-NN & 92.29 & 93.97 & 94.56 & 90.47 & 91.89 & 94.72 & 94.29 & 96.28 & 94.41 \\ RF & 91.42 & 92.26 & 94.62 & 93.43 & 94.16 & 94.72 & 89.54 & 90.46 & 94.57 \\ AB & 89.33 & 95.15 & 96.02 & 90.07 & 93.92 & 96.64 & 88.79 & 96.45 & 95.52 \\ GBDT & 89.10 & 92.42 & 94.58 & 87.28 & 93.05 & 94.22 & 91.63 & 91.68 & 95.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of the testing stage (all scores are in percentile). The result of the study shows that all the algorithms perform better, when the data set includes features from both channels rather than when they are considered individually. Among the three data sources, all the algorithms performed weakly on the 18 kHz data set comparatively. The performance of the algorithms based on F1-score was higher for the 27 kHz data set and for data set with combined features. In railway industry, it is essential to balance the risk of failure and the cost of inspection. Therefore, it is necessary to have a good balance between the precision and recall of the detection algorithm. Both channels are preferred for detection of railway fastening systems as the algorithms performed better and exhibited a good balance between precision and recall, when the features of both channels were used for representing an individual clamp. Among the six algorithms tested, AdaBoost, which is a type of ensemble algorithm, slightly outperformed the other four algorithms in all evaluation matrices. Further research will be carried out to incorporate other classification techniques (e.g., artificial neural network, XGBoost, etc.) that can have the capability of improving the predictive strength for fastener detection. The possibilities of unsupervised clustering for anomaly detection will also be investigated in the further studies. The current study incorporates only one type of fastener, namely Pandrol e-clip. The future of this study will also focus on different types of fasteners that can be distinguished by the rotation angle, as different fastening systems have different geometrical shapes.