## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Suspension Damage Identification  
[PERSON] et al. [67] proposed the use of a multiple-model-based approach to the assessment of suspension faults on a railway wagon. The vehicle was modeled grounded \begin{table}
\begin{tabular}{l c c l} \hline
**References** & **Scope** & **Study Type** & **Methodology** \\ \hline \multirow{4}{*}{[PERSON] et al. [49]} & \multirow{2}{*}{RC} & \multirow{2}{*}{N} & \multicolumn{2}{l}{Parametrical study based on numerical simulations to assess the influence of vehicle speed and track natural frequency on squat-related frequencies at the axle box.} \\  & & & Use of the magnitude of the sum of wavelet transform coefficients as a damage feature \\  & & & Power spectrum derived from the Wavelet transform associated with classification algorithm based on several condition statements for joint bolt tightness estimation. \\  & & & Use of a 1D convolution neural network based on acceleration measurements on axle boxes for corrugation identification and classification by wavelength and a kriging surrogate model for corrugation depth estimation \\  & & & Renvi entropy of the packets resulting from a signal decomposition by the wavelet packet transform and a support vector machine for corrugation classification. \\  & & & Kriging surrogate model built based on the energy of corrugation-relevant packets after applying wavelet packet transform to sound pressure recordings \\  & & & Signals power derived from the Wavelet spectrum for the frequency range of acoustic emission associated with corrugation \\  & & & Rail condition assessment based on several features, namely (i) \\ \multirow{2}{*}{[PERSON] et al. [57]} & \multirow{2}{*}{RC} & \multirow{2}{*}{N/E} & \multicolumn{2}{l}{Time-frequency representation, (ii) Distance-frequency representation;} \\  & & & (iii) Distance-amplitude representation; and (iv) Signal Energy. \\ \multirow{2}{*}{[PERSON] et al. [58]} & \multirow{2}{*}{RC/RS} & \multirow{2}{*}{A/N} & \multicolumn{2}{l}{Square of the instantaneous amplitude derived from the Hilbert transform (IAS)} \\  & & & Reverse modeling for derivation of the track’s stiffness profile by the application of the cross-entropy optimization algorithm \\ \multirow{2}{*}{[PERSON] et al. [60]} & \multirow{2}{*}{RS} & \multirow{2}{*}{N} & \multicolumn{2}{l}{Use of variations in the natural frequency of the train track interaction system} \\  & & & Square of the instantaneous amplitude derived from the Hilbert transform (IAS) \\ \multirow{2}{*}{[PERSON] et al. [61]} & \multirow{2}{*}{RS} & \multirow{2}{*}{A/N} & \multicolumn{2}{l}{Square of the instantaneous amplitude derived from the Hilbert transform (IAS)} \\  & & & Transform (IAS) \\ \multirow{2}{*}{[PERSON] et al. [62]} & \multirow{2}{*}{RC/RS} & \multirow{2}{*}{A/N} & \multicolumn{2}{l}{First natural frequency of rail vibration derived from the Fourier transform of contact point responses} \\ \multirow{2}{*}{[PERSON] et al. [63]} & \multirow{2}{*}{RS} & \multirow{2}{*}{N/E} & \multicolumn{2}{l}{Discontinuities in the filtered bogie displacement response calculated by the integration of acceleration signals measured at the bogies} \\ \hline \hline \end{tabular} Abbreviations: Track irregularities (TI), rail condition (RC), rail supporting elements condition (RS), analytical (A), numerical (N), and experimental (E).  
\end{table} 
Table 2: _Cont._on multibody formulations and several different models were proposed, each one considering a different type of suspension failure, and the failure of a particular measurement equipment was also considered. The damage identification was then performed by finding, through a Kalman Filter, the model that best represents the measured outputs over time, which in this particular case were bogie and car body lateral acceleration and the yaw rate of the bogie. The performance of the method was evaluated for two failure types, a faulty side damper, and a malfunctioning lateral bogie accelerometer.