## 2 Research Methodology
### Data Collection  
The data for this study were collected along the northern loop of the heavy haul line at Katterjakk and Stordalen, close to the Sweden-Norway border. The differential eddy current sensor was mounted 65 mm above the railhead on a trolley system and was made to run along the track, using a motor (refer Figure 2a). The speed of the trolley system varied between 1.3 m/s to 2 m/s. The sensor system, which is used to measure one side of the track, consists of two differential eddy current sensors with a driving field of 18 kHz and 27 kHz respectively placed at a distance of 20 cm apart in the travel direction. The above-mentioned carrier frequencies are selected as they fall under the rail norms. Each sensor consists of two differentially coupled pickup coils (P1 and P2) that are enclosed by the driving coil. The direct cross talk between the driver and the pickup coils within an individual sensor is cancelled, though not completely, by the differentially coupled pick-up coil. The inbuilt cross-talk cancellation (CTC) function within the sensor system cancels out the small cross talk between the two-carrier frequencies as they have a common factor of 9 KHz. The entire unit is vacuum potted with epoxy resin to stabilize the sensor, both against vibrations and to reduce temperature drift. The output voltage from an individual sensor is the result of the cross-talk residue between the driving coil and the pickup coils, and the induction of eddy currents along the rail, which are linearly superimposed. The differentially coupled pickup coils (P1-P2) are sensitive only to the changes in the eddy current in the rail and its vicinity. The resulting voltage will be zero if there is no change in either conductivity (\(\upsigma\)), magnetic permeability (\(\upmu\)) or geometric form of the measured material, e.g., an ideal rail with no clamps or any surface defects.  
The sensor was powered using a 12V62 AH battery and the measured raw data were recorded using a laptop. The considered track sections consisted of concrete sleepers with E-clip fasteners. Tracks which were relatively healthy and track sections with damages were all considered for this study. Some of the visible damages on the rail head included squats, rail corrugation, crack and head checks. Figure 2b depicts a fastening system with intact clamps, a fastening system with one missing clamp and a fastening system with two missing clamps. A controlled measurement sequence was carried out along the track section to obtain the dataset for the e-clip fastening system. A pattern of missing clamps was created, along a measurement sequence, where clamps were removed from the outer and the inner side as well as from both sides at the 20 th, 25 th and 30 th sleeper respectively, from the starting position of the measurement (refer Figure 3). This measurement sequence was carried out along various sections of the track. A fastening system with intact clamps was considered as a healthy system and fastening systems with one clamp missing (stage 1 fault) and two clamps missing (stage 2 fault) were considered as faulty system for this study.  
A total of 2967 fastener signatures were used for this study, of which 2700 samples (91%) of the instances correspond to a healthy state (stage 0) with intact clamps, 168 samples  
Figure 1: Generic fastener detection framework using machine learning algorithm.  
(5.67%) correspond to faulty fasteners with one clamp missing (stage 1) and the remaining 99 samples (3.33%) represent a faulty state with both clamps missing (stage 2).