## 3 Application of Drive-by Methodology on the Railway Track Subsystem
### Track Irregularities Assessment
Wavelengths shorter than 5 m, however, could not be captured due to the filtering effect of the suspensions and were removed with a high pass filter. [PERSON] et al. [39] also made use of deep learning techniques for assessment of the longitudinal level of high-speed railway lines in China. The authors proposed a NN model combining an Attention Mechanism (AM), a Convolution Neural Network (CNN) and a Gated Recurrent Unit (GRU), which was trained using measured data from a specialized inspection vehicle. The inputs for the model are the vertical and lateral acceleration measured at the car body and the vehicle speed, and the output is the track longitudinal alignment. Good results were obtained when compared to measured irregularities; however, just as in the previous study, the filtering effect of the suspensions makes it impossible to assess low  
Figure 3: Experimental setup for longitudinal level assessment: (**a**) positioning; (**b**) accelerometer and gyro-meter installation and (**c**) data acquisition system [37]. wavelength irregularities. In this case, the author succeeded in estimating wavelengths above 3 m.  
Unlike previous works that focused on obtaining the profile of irregularities, [PERSON] et al. [40] took advantage of the close relation between vertical irregularities and bogie vertical acceleration, in order to train a linear regression model capable of inferring track quality from the RMS value of bogie acceleration. Two models were trained, one to estimate the RMS value of vertical alignment, and the other to obtain its peak value, where greater accuracy was obtained for windows of 100 m and 25 m, respectively. As expected, the estimation of the peak results was not as accurate as that of the RMS values, although it presented reasonable precision to infer the quality of the track. [PERSON] [29] also proposed a method based on RMS values but using the combined information of vertical and lateral car body acceleration and car body roll rate. Based on these data a machine learning algorithm, particularly the support vector machine (SVM), was used to detect track faults. The methodology was validated both numerically and experimentally on an operational train, and the degradation associated with the longitudinal level, alignment, and cross irregularities was successfully identified. [PERSON] & [PERSON] [27] used the maximum RMS value of car body vertical acceleration, calculated for small track sections, as an indicator of track fault. For track segments identified as critical, a detailed time-frequency analysis, based on the Hilbert Huang Transform, was used to classify the type of defect. The longitudinal level degradation was shown to be associated with frequencies below 5 Hz, making it distinguishable from localized defects, which are more related to a higher frequency range. In an investigation involving similar track faults, [PERSON] & [PERSON] [41] demonstrated the feasibility of applying Convolutional Neural Networks directly to the time-frequency color map representation to automatically identify and distinguish between these defects. Still concerning the use of machine learning algorithms for general track quality assessment, [PERSON] et al. [42] investigated the performance of three different classifiers (the decision tree and SVM associated with linear and Gaussian kernel functions) on  
Figure 4: Track longitudinal level prediction based on EAE: (**a**) EAE architecture, (**b**) results for the simulated training set, and (**c**) results for the real training set [38]. inferring the standard deviation of alignment and cross-level track irregularities. The models were trained based on a simulated data set where the standard deviation of lateral and roll bogie frame accelerations were used as inputs and the track class is given as output. After training, the algorithms were tested based on measured data, and the SVM, in both versions, presented a better overall performance for this particular application compared to the decision tree.