## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Wheelset Damage Identification
The combined use of both techniques (EEMD and WVD) helped eliminate numerical errors and provided very accurate detection of the frequency associated with the defect. However, comprehensive experimental tests are still needed to verify the accuracy of the method against environmental effects and nonlinearities associated with suspensions and ballast behavior. Instead of using the time domain, [PERSON] et al. [84] took advantage of the cyclic nature of wheel defects and proposed a damage feature based on the axle box acceleration as a function of the wheel rotation angle. A simple and effective alternative was adopted to eliminate the influence of non-cyclic components (not associated with wheel damage), calculating the average response for several wheel revolutions. The method was validated both numerically and experimentally and good results were achieved in detecting the damage severity and the polygon order. Remaining with the wheel OOR defect, [PERSON] et al. [85] proposed a complete and fully automatic methodology for assessing this type of damage based on a deep learning model. The main steps of the methodology are represented in Figure 15a: firstly, wheelest acceleration time histories are measured and a feature extraction step is performed on this data. Then, a neural network-based deep learning model called OORNet was designed and trained based on a large dataset of axle box acceleration, generated through numerical simulation, comprising 2000 different wheel OOR defects, where 70% of the samples were used for training and 30% for testing the accuracy of the model. The model's architecture is schematically described in Figure 15b, comprising the operations performed in each step, as well as the size of the corresponding data arrays. As shown in the first layer, three types of features were used as inputs for the model: the upper envelope of the time domain acceleration signal, the FFT spectrum, and the time-frequency representation derived from the Wavelet transform. The features are then processed by 1 DCNNs for FFT and amplitude, and 2 DCNNs for the Wavelet transform. The result of this processing is then merged and processed by a Fully Convolutional Neural Network (FCNN) and a 3D CNN. Finally, the model returns two types of outputs. the predicted roughness profile of the wheel and a Boolean (faulty or healthy) indicator. based, respectively, on regression and classification strategies. During the testing phase, the model could estimate very accurately the wheel roughness profile and, as can be seen in the confusion matrix of Figure 15a, an accuracy of 97% was reached for the defect identification. The perfo  
Figure 14: Wheel out-of-roundness (OOR) defects [84]. also compared with the other three deep learning models using different features and has proved to be superior. However, the model has been shown to be very sensitive to speed fluctuations, which is an issue needing a solution. Before the transition to practical applications, data simulated for different train speeds during the training phase may be included. Another type of issue related to the wheels is their uneven wear in the same bogie, usually called wheel diameter difference (WDD), which is a recurring problem in wagons operating in heavy-haul lines. To monitor this condition, [PERSON] et al. [86] proposed the use of the lateral axle box acceleration signal and developed an automatic methodology, depicted in the framework of Figure 16, to extract in-phase WDD (on the same bogie side) and anti-phase WDD (on different sides of the bogie). The feature extraction was performed based on the combined use of the Adaptive Chirp Mode Decomposition (ACMD) signal decomposition algorithm and the calculation of the Fractal Box Dimension (FBD). Then, a machine learning algorithm, the Multiple Kernel Extreme Learning Machine (MKELM), was trained for feature classification between normal operation, anti-phase WDD, and in-phase WDD. The methodology was validated based on numerical and measured data and accuracies above 95% were obtained for all cases.