## 5 Conclusions
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Scope** & **Study Type** & **Methodology** \\ \hline Mei \& Ding [(70)] & VS & A/N & Coupling between bounce and pitch movements of bogies under suspension fault conditions \\ [PERSON] et al. [(71)] & VS & N/E & RMS value of bogie acceleration under normal operating conditions and for a primary suspension damper failure \\  & & & Multiscale algorithm permutation entropy for feature extraction is associated with a dimensionality reduction technique based on Manifold learning, particularly the local linear tangent space alignment, and a Least Squares Support Vector Machine for classification. \\ [PERSON] et al. [(73)] & VS & N/E & 1D convolution neural network for feature extraction and a fully connected neural network for classification \\ [PERSON] et al. [(74)] & WS & N & Harmonics proportional to the speed of the axis in the displacement signal of the axle boxes for axle crack detection \\ [PERSON] et al. [(75)] & WS & E & Energy from Packets correlated with damage after applying a Wavelet crack detection \\ [PERSON] et al. [(76)] & WS & E & Energy from Packets correlated with damage after applying a Wavelet crack detection \\ [PERSON] et al. [(76)] & WS & E & package Transform and a support vector machine as a classifier for axle crack detection \\ [PERSON] et al. [(77)] & WS & N/E & The prominence of peak acceleration on axle boxes compared to the base level for wheel flat detection \\ [PERSON] et al. [(78)] & WS & N/E & Kriging surrogate interpolation model correlating peak acceleration, vehicle speed, and wheel flat length for assessment of the flat length \\ [PERSON] et al. [(79)] & WS & E & Convolution neural networks used for wheel flat detection from car body accelerations \\ [PERSON] et al. [(80, 81)] & WS & E & An analog device equipped with accelerometers and a peak counter for identifying periodic patterns associated with wheel flat \\  & & & Time-frequency representation obtained through the application of the \\ [PERSON] et al. [(82)] & WS & N & Ensemble Empirical Mode Decomposition and Wigner-Ville algorithm \\  & & & Distribution for wheel out-of-roundness detection \\ [PERSON] et al. [(84)] & WS & N/E & Average of several revolutions of acceleration on axle boxes plotted as a function of axle rotation angle for wheel out-of-roundness detection \\ [PERSON] et al. [(85)] & WS & N/E & Fourier transform, amplitude of the signal envelope, and time-frequency representation derived from the Wavelet transform as inputs for a neural network-based model to detect wheel out-of-roundness \\ [PERSON] et al. [(86)] & WS & N/E & Fixed Box Dimension calculated from signal decomposition by adaptive chirp mode decomposition and the multiple kernel extreme learning machine used as a classifier for wheel diameter difference detection \\ [PERSON] et al. [(87)] & WS & N & Use of constrained unscented Kalman filter for estimating the equivalent \\ \hline \hline \end{tabular} Abbreviations: Vehicle suspension (VS), wheelset (WS), analytical (A), numerical (N), and experimental (E).  
\end{table} 
Table 3: _Cont._some authors working on the development of embedded devices with good perspectives for achieving the commercial application of these technologies. It is also relevant to point out the increased number of authors that are proposing methodologies based on artificial intelligence algorithms to identify anomalies in both track and vehicle. Inevitably, due to the long extensions of the railways and the high demand for vehicles, the feasibility of these methodologies involves the processing and interpretation of large volumes of information. Thus, from the practical point of view, making the application of these methodologies fundamentally feasible depends on developing techniques that allow an automated interpretation of the results, which is already being performed by several authors.