## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Wheelset Damage Identification
was validated numerically and experimentally for an instrumented tank wagon in which a 20 mm wheel flat was artificially created. The method was able to estimate the length with a maximum error of 5% and 10% for the numerical and experimental validations, which is a very good accuracy considering all the uncertainties involved in the practical application. Differently from previous studies, which used axle box acceleration, [PERSON] et al. [79] designed and trained a 1D lightweight Convolutional Neural Network (CNN) to detect wheel flat from car body acceleration. The proposed architecture was composed of three parts: (i) down-sampling, (ii) feature extraction, and (iii) classification. Despite being more difficult, a methodology based on accelerations in the car body may prove to be useful as it can take advantage of some instrumentation that already exists in the vehicle. The performance of the designed CNN was compared with other state-of-the-art architectures and proved to perform better with accuracies above 95% for speeds between 25 km/h and 85 km/h; however, for speeds outside this range the accuracy drops significantly. The influence of wagon and track conditions was not addressed but could have negative effects on the model accuracy. While all the aforementioned approaches rely on digital signal processing techniques, which inevitably require processing units that consume a certain amount of energy, [PERSON] et al. [80] focused on the development of a fully analog wheel flat detection system with very low energy consumption, compatible with energy harvesting technologies. This approach is particularly interesting for applications involving long freight trains, in which it is not always possible to keep electrically powered acquisition and processing units in all wagons. Specifically, the system is composed of a circuit that emits a high voltage signal when acceleration reaches a certain limit, coupled with a counter to ensure that the disturbance is periodic. Finally, the device outputs only a Boolean signal indicating the presence or absence of damage. The proposed device was tested experimentally by [PERSON] et al. [81] using a scaled model of a railway bogie. The tests occurred for speeds of 40 km/h and 60 km/h, which are quite typical for heavy haul freight vehicles and the device was able to efficiently identify the presence of wheel flats. Still concerning non-circular wheel wear, the assessment of wheel out-of-roundness (OOR) based on onboard instrumentation was also explored by some authors. This type of defect consists of non-uniform wear on the wheels, causing them to lose roundness and take on polygonal shapes, which is schematically represented in Figure 14. The polymerization, as depicted in the left diagram of Figure 14, can have different severities based on the amplitude of the wheel imperfections. The defect can also assume different shapes associated with the number of sides of the polygon (order); this situation is represented on the right part of the diagram in Figure 14 for the third and twentieth orders. This loss of roundness leads to excessive vibrations and, consequently, discomfort for passengers and excessive wear of vehicle and track components. In theory, the polygonization of the wheels  
Figure 13: Experimental setup for wheel flat detection [77]. is responsible for generating typical frequencies as a function of vehicle speed and the order of the polygon. Based on this theory, [PERSON] et al. [82] proposed a time-frequency methodology for detecting OOR on the wheels of high-speed trains. The authors suggested the combined use of the Ensemble Empirical Mode Decomposition (EEMD) algorithm [83] and the Wigner-Ville Distribution (WVD), which is a powerful means of time-frequency signal processing for analyzing nonstationary signals. The method was tested with simulated axle box acceleration data considering track irregularities.