**6. Conclusions**

The application of recurrence plots to the dynamic analysis of vehicles, in this case, a scaled-down train, showed that variations on the track condition modified the graphical representation, and the recurrence quantification analysis demonstrated that these variations produced significant changes in the quantification parameters.

For producing the recurrence plots, acceleration measurements were recorded on the train's platform while traveling along a closed circuit. The vertical acceleration data were divided into the rigid motion modes (identified as low-frequency vectors) and the vibration modes (identified as high-frequency vectors).

The train's trajectory was divided into short segments to have cleaner graphs, approximately every six sleepers. This segmentation allowed the location of the defects within the time series (acceleration data). It was easier to locate the changes in curvature using the gyroscope data since it precisely showed the instants when the railcar changed direction and entered a curved section.

The recurrence plots were produced from the phase plane of each segment. The phase planes were calculated by separating the acceleration data into intrinsic mode functions using the empirical mode decomposition, applying the delay principle (wave shifting) to each intrinsic mode functions, and reconstructing the signal by adding them again. Before the addition, the intrinsic mode functions were grouped into two, one for the intrinsic mode functions associated with the rigid motion accelerations and the second for high-frequency vibration modes.

The high-frequency vector showed higher sensitivity to excitations. The track had several track joints, changes of curvature, and defects on the base plate. The recurrence plots showed different topologies that were considered "signatures" in all the cases.

The parameter that has a higher sensitivity for identifying defects on the track is the Shannon entropy. It measured the disorder on the recurrence matrices and provided a measurement tool for monitoring variation on the track or the railcar.

Further work should focus on automating the topology analysis and the definition of methodologies for predicting faults and defects using intelligent sensors on the railcar.
