## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Suspension Damage Identification
The method has proven its potential to be an efficient alternative for detecting suspension damage; however, its performance still needs to be verified under the influence of environmental and operational disturbance inherent to practical applications. A similar approach was adopted by [PERSON] et al. [68], in which the recursive least square (RLS), an adaptive filtering algorithm closely related to the Kalman Filter, was used to estimate the stiffness and damping constants of a railway vehicle from dynamic measurements taken at the bogie and axle boxes. The validations based on simulated and measured data revealed that the RLS is capable of providing reliable estimates under noise and uncertainties in the parameters. The accuracy of model-based approaches, such as those previously presented, tend to strongly rely on high-quality numerical models, which are not always available and may impair the accuracy of the method. [PERSON] et al. [69] performed a comparative study comparing some common model-based formulations and data-driven ones: the robust observer, the Kalman filter combined with the generalized likelihood ratio test method, the dynamical principal components analysis, and the dynamical canonical variate analysis. All approaches were applied to the suspension fault detection problem, and the authors concluded that, in general, data-driven methods tend to outperform model-based methods. Thus, some authors have tried to overcome this difficulty by proposing data-driven approaches. [PERSON] & [PERSON] [70] demonstrated analytically that under normal operating conditions, that is when all the springs and dampers of a bogie have the same properties, the bounce and pitch movements of the bogie are uncoupled. Based on this, the authors proposed the use of cross-correlation functions to identify possible couplings between these movements that might indicate the existence of damage in some suspension components. [PERSON] et al. [71] investigated the RMS values of bogie acceleration under normal operating conditions and for a primary suspension damper failure. A simulated and measured dataset of the vehicle without any damage was used to establish a zone associated with the normal operating condition, and values outside this zone were assigned to damage scenarios. To test the methodology, several damage scenarios were simulated and the method was capable of detecting damage causing a reduction of 50% or more in the damping coefficient. A more complex approach to identifying different types of suspension faults, i.e., anti-yaw dampers fault (YDF), lateral dampers fault (LDF), and yaw & lateral dampers fault (Y&LDF), was developed by [PERSON] et al. [72]. The multiscale permutation entropy (MPE) algorithm was used as a feature extractor; this algorithm consists of a group of Permutation Entropy (PE) values that capture randomness and dynamic changes of the time series. Thus, a Manifold learning algorithm, particularly the linear local tangent space alignment (LLTSA), was used to reduce the dimensionality of the feature matrix generated by the MPE. Finally, a Least Squares Support Vector Machine (LSSVM) model was trained to classify the between scenarios of normal operation, YDF, LDF, and Y&LDF. The method was validated numerically and experimentally and was able to precisely classify the damages. Furthermore, a comparison between the performance of the LLTSA and the Principal Component Analysis (PCA) was performed, and the LLTSA exhibited better performance against the non-linearities inherent to railway systems. The steps of the proposed methodology are depicted in Figure 11.  
More recently [PERSON] et al. [73] trained a 1 DCNN model to extract features associated with suspension damage and, based on these features, a fully connected NN was used to classify the same suspension faults addressed by the previous study. In this study, the influence of different track irregularities and the state of wear on train wheels on the model accuracy was investigated.