###### Abstract  
The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage. clamps; rail fastening system; differential eddy current sensor; machine learning; classification +
Footnote †: journal: Medical  
improve the rail performance could be capital expansion of the infrastructure, but this is a time consuming and cost-intensive approach. Hence, in order to improve the capacity, availability and the service quality of the existing infrastructure, an ideal solution would be to improve the maintenance and renewal (M&R) process. The quality of the infrastructure and the utilization methods play a crucial role in determining the operational capacity of a given railway infrastructure [5]. The dependence between operational capacity and the condition of the infrastructure is a crucial aspect in railway infrastructure maintenance. When the railway infrastructure is in a good state with high quality, a higher operational capacity with higher quality of service is achieved. As the operational capacity increases, the infrastructure is subjected to more traffic and load, which leads to deterioration of the infrastructure and deformation of its components. Therefore, higher-frequency maintenance and renewal are required. These demand track possession, which in turn reduces the operational capacity. The down time arising from maintenance and renewal of networks is responsible for nearly half of all the delays to passengers. To investigate the actual delay within Sweden, the records of the infrastructure manager was analysed. Tables 1 and 2 show the delay in hours for 10 years, due to failure of components in track and switches and crossings (S&C) respectively. The delay time used for this study only includes the downtime of service arising due to the corrective maintenance employed to fix the fault or failure. The downtime arising due to corrective maintenance is unplanned and can occur at any given point of time, having consequential effect on traffic. On the other hand preventive maintenance actions are planned, i.e., the track occupation is guaranteed without traffic interference. However, in a few instances, preventive maintenance can occupy the track longer than the allocated time frame, thereby leading to traffic interference or delay [6], which is not included in the present study.