## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Wheelset Damage Identification  
Another field of application for onboard monitoring systems involves the detection of damage related to the wheelset components, more specifically the axle and the wheels. Regarding axle defects, [PERSON] et al. [74] conducted a numerical study to verify the feasibility of detecting cracks on a railway axle from displacement signals on several points along the axle. Two different axles, with hollow and solid sections, were modeled using solid finite elements. A crack was introduced in seven different locations along the axle, resulting in one different model for each crack location, and numerical simulations of its operation were  
Figure 11: Methodology for suspension fault identification [72]. performed. Five points along the axle were chosen as candidates for measuring and it was concluded that using data from two points, one in each axle box, is sufficient for damage detection. The presence of a crack on the axle resulted in strong harmonic components at these points, which could be clearly distinguished from data obtained for an undamaged axle. Besides having obtained good results, the axle was isolated during the simulations and further investigations on the influence of operational interference are needed; additionally, displacement data were used to assess the damage, but experimentally measuring this quantity is always more complicated. [PERSON] et al. [75] proposed the use of the WPT of acceleration signals measured at the axle box of a railway bogie to detect the presence of a crack at the axle. Three accelerometers were positioned at the axle box of a real Y21 bogie (Figure 12a) and a set of tests was conducted with the operating bogie isolated on a specialized platform. During the tests, cracks with different depths were inserted into the axle (Figure 12b) to simulate the damage. A sensitivity analysis was performed for investigating the degree of correlation between the energy content of the wavelet packets and the damage severity. The most sensitive packets were chosen as suitable features to assess the damage. Based on these features, an Artificial Neural Network (ANN) was trained for automatic damage assessment. An accuracy of 96% was achieved and the rate of false alarms remained at around 5%. The same type of feature and experimental setup was used by [PERSON] et al. [76] to train a machine learning algorithm, more specifically the Support Vector Machine (SVM), to identify cracks on the axle. Good results were also obtained, with accuracy above 90% for cracks compromising more than 6.5% of the cross-section. Regarding wheel-related defects, [PERSON] et al. [77] developed an algorithm for wheel flat detection which relies on a damage indicator based on the prominence of peak acceleration in axle boxes above the baseline RMS level. Due to the cyclic nature of disturbance caused by wheel damage, the authors suggested the computation of an average acceleration signal over several wheel revolutions to avoid interference from possible track-related defects. The algorithm was validated by numerical simulations considering a track with large irregularities and also experimentally by a set of sensors installed on a freight wagon bogie (Figure 13). The method has proven to be capable of efficiently detecting flats in the early stage and inferring their severity, which is proportional to their length. Regarding the flat length, [PERSON] et al. [78] proposed a data-driven approach for its assessment which relies on the construction of a KSM model to estimate peak axle box acceleration for a given vehicle speed and wheel flat length. The model was developed based on several numerical simulations performed with different combinations of flat length and vehicle speed. Once the model was developed a Particle Swarm Optimization (PSO) algorithm was used to obtain the flat length from measured peak axle box acceleration and vehicle speed. The methodology  
Figure 12: Railway axle crack identification: (**a**) experimental setup and (**b**) introduced crack [75].