## 3 Results and Analysis  
Figure 4a,b shows the time signal plot after demodulating, resampling, filtering and rotating the raw signal, for both the driving field for a single measurement sequence. The measurement was carried along a short section of the track having 47 sleepers. This section of the track was relatively healthy without any damages to the railhead. The zero crossing in the signal from the positive to the negative induction represents the centre positioning of the fastening system. The zero crossing was used as a way to segregate individual fastening systems. Individual fastening systems are easily distinguishable from the time signal plot for both driving channels. A drop in amplitude of the fastener signature is visible at those positions where the clamps were missing from the fasteners. Missing clamps cause reduction in the metallic material and changes the geometry of the fastening system, thus reducing the amplitude of the return field. The healthy fastening systems with intact clamps are depicted using black markers and fastening systems with one clamp missing and fasteners with both clamps missing are represented using blue and red markers respectively. Figure 5 depicts features pertaining to individual fasteners for both channels, obtained from the above time signal (refer Figure 4). All the four features, for both channels showed a drop in their value, when the clamps were missing from the fastening system. The  
Figure 4: Time signal after demodulating, filtering and rotating the raw signal: (**a**) 18 kHz, (**b**) 27 kHz. Fastening systems with one clamp missing are depicted with blue markers and fasteners with both clamps missing are depicted using red markers. Healthy clamps are indicated with black markers. above plots are obtained for a short track section where the difference between feature levels are easily distinguishable. However, this clear difference between the three classes is not as evident for all track sections where additional disturbances can be present as observed in Figure 6. Figure 6 shows the histogram plot (in probability density function) for all the eight features for the entire data set of fasteners (from both 18 kHz and 27 kHz channel). The healthy fasteners are indicated within the black markers and fasteners with one or both clamps missing are marked in blue and red respectively. It is evident from the plot that there are significant overlaps between the three areas for all the features. The disturbances in track can affect the induced voltage in the eddy current sensor, thus causing fluctuations in the features. The overlap is seen in features extracted from both channels. It becomes a difficult task to mark a boundary or threshold to differentiate the state of fasteners and use individual features for classification purposes. This calls for the use of machine learning algorithms that can combine multiple features to create a boundary or threshold for classification purpose. Figure 7 depicts the 3-D representation of the overall data set used for classification using three random features for the 18 kHz channel. Figure 5: Standardised features for the measurement sequence (Figure 4) extracted from: (**a**) 18 kHz channel, (**b**) 27 kHz channel. Fastening systems with one clamp missing are depicted with blue markers and fasteners with both clamp missing are depicted using red markers. Healthy clamps are indicated with black markers. Figure 6: Histogram plots in probability distribution function with respect to standardized feature values for all features: (**a**) Arc length of complex signal-18 kHz, (**b**) Arc length of complex signal-27 kHz, (**c**) RMS-18 kHz, (**d**) RMS-27 kHz, (**e**) Peak to peak-18 kHz, (**f**) Peak to peak-27 kHz, (**g**) Magnitude at clamp frequency-18 kHz, (**h**) Magnitude at clamp frequency-27 kHz. Fastening systems with one clamp missing are depicted with blue markers and fasteners with both clamp missing are depicted using red markers. Healthy clamps are indicated with black markers.