## 3 Application of Drive-by Methodology on the Railway Track Subsystem
### Summary of Discussed Literature on Drive-by Methodologies Applied to the Track Subsystem  
In Table 2, a summary of the main references discussed in the previous sections regarding the application of drive-by methodology on the railway track subsystem is presented. Aspects such as the type of damage detected, study type, and main techniques used are highlighted for a better consolidation of the contents presented.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Scope** & **Study Type** & **Methodology** \\ \hline [PERSON] et al. [30; 31] & TI & A/E & Analytical expressions between dynamic responses and irregularities for extracting the profile of irregularities based on measurements of gyroscopes and accelerometers in the bogies. \\ [PERSON] et al. [32] & TI & A/E & Inverse modeling based on frequency response functions between dynamic responses and irregularities to extract irregularity profiles. Inverse modeling based on Kalman filters and state space models to extract irregularities \\ [PERSON] et al. [25] & TI & N & Inverse modeling based on Kalman filters and state space models to extract irregularities \\ [PERSON] et al. [34] & TI & E & Two formulations in the time domain, one based on the use of Kalman filters and the other on the Unknown Input Observer, and one frequency domain approach based on the inversion of FRFs for lateral alignment assessment \\ [PERSON] et al. [35; 36] & TI & N & Reverse modeling based on cross-entropy optimization algorithm to obtain the profile of irregularities that makes the simulated response closer to the actual responses \\ [PERSON] et al. [37] & TI & E & Weverse modeling based on cross-entropy optimization algorithm to obtain the profile of irregularities that makes the simulated response closer to the actual responses \\ [PERSON] et al. [38] & TI & N/E & Using extended auto encoders to find a latent representation of irregularities based on measured accelerations \\ [PERSON] et al. [39] & TI & N/E & Deep neural network model to infer longitudinal alignment profile based on measured responses \\ [PERSON] et al. [40] & TI & E & Linear regression model between RMS acceleration on bogies and RMS irregularities \\ [PERSON] [29] & TI & N/E & Use of a support vector machine to classify irregularities severity from RMS acceleration on bogies \\ [PERSON] & [PERSON] [27] & TI & N/E & Identification of potentially damaged areas by RMS acceleration at the bodies and detailed investigation of these areas by Hilbert Huang’s Transform \\ [PERSON] & [PERSON] [41] & TI & N/E & Recognition of damage patterns in Wavelet time-frequency representation trough convolutional neural networks \\ [PERSON] et al. [42] & TI & N/E & Support vector machines and decision trees to classify faulty conditions from the standard deviation of lateral and rolling accelerations of bogies \\ [PERSON] et al. [43] & TI & E & Instantaneous amplitude derived from the analytical signal calculated using the Hilbert transform \\ [PERSON] et al. [45] & RC & N & Time-frequency representation derived from Wavelet transform of axle box acceleration \\ [PERSON] et al. [46] & RC & E & Time-frequency representation derived from Wavelet transform of axle box acceleration \\ [PERSON] [47] & RC & E & Performance comparison between time-frequency representation derived from Wavelet transform and short-time Fourier transform STFT of axle box acceleration in rail condition assessment \\ [PERSON] et al. [48] & RC & E & Use of the Wavelet power magnitude in the frequency range associated with the squat as a damage feature \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the presented works in drive-by methodology on the track subsystem.