## 3 Application of Drive-by Methodology on the Railway Track Subsystem
### Rail Condition-Based Assessment  
Acceleration measurements on vehicles can provide good indications of the presence of localized defects that affect the surface geometry of the rails, such as Squats and Crushed Heads, which are common defects associated with rolling contact fatigue (RCF). A comprehensive review of these RCF faults is presented by [PERSON] [44]. This type of defect causes impact excitations in the wheel-rail contact, which can be identified by the acceleration measurements. [PERSON] et al. [45] conducted studies aimed at determining the causes and evolution of squat-type defects. The dimensions of several defects of this type found in the Dutch railway network were analyzed. Based on the typology of these defects, numerical simulations of the passage of the wheel over the defects were carried out, through which it was demonstrated that there is a close relationship between wavelengths in the contact forces and the dimensions of the squats. Based on these studies, it was predicted that Squat initiation and growth are typically associated with certain wavelengths between 20 and 40 mm. These predictions were further validated experimentally by [PERSON] et al. [46], who used the Wavelet transform of acceleration records, measured at the axle box of a railway vehicle traveling at 110 km/h, to identify Squat related frequencies. The use of the Wavelet transform instead of the Short Time Fourier Transform (STFT) provides better time and frequency resolutions. The robustness of this signal processing technique for track damage identification was investigated in depth in the pioneering work developed by [PERSON] [47], and its advantages compared to STFT. In Figure 5, a Squat defect and the associated axle box Wavelet acceleration spectrogram are presented. As can be seen, the presence of the defect results in an increased level of energy around high frequencies, especially around 1500 Hz, which is consistent with the wavelengths found to be associated with Squats in the work conducted by [PERSON] et al. [46]. Based on the aforementioned investigations [45; 46], [PERSON] et al. [48] proposed an automatic methodology for Squat detection based on three-dimensional acceleration measurements taken at the four-axle boxes of a railway bogie (Figure 6). To enable practical applications, the authors used signal filters for removing noise from the data and proposed a procedure for eliminating the contribution of wheel defects based on revolution period, which is a function of the wheel radius and wagon speed. Then, automatic damage detection is performed based on an empirically defined threshold value for the Wavelet power magnitude in the frequency range associated with squats. With the proposed methodology, the authors achieved a 100% hit rate for moderate and severe squats and 78% for small defects. The method, however, is sensitive to track discontinuities, such as weldsand joints and vehicle speed. Regarding vehicle speed, and different from the applications related to bridges, higher speeds seem to be beneficial for the identification of rail faults, as they result in greater vibration energy when the wheel passes over a defect. The effect of the vehicle speed was investigated in greater detail in parametric studies conducted by [PERSON] et al. [49], where a relation between speed and axle box acceleration at squats was derived. Additionally, the authors demonstrated that the frequencies at squats are somehow linked to the natural frequency of the track. This particular study raised important aspects to be addressed for the practical application of squat detection. Given the dependence of the responses associated with Squats to the speed and natural frequency of the rail, it is essential to establish a precise mapping between these quantities to support automatic identification algorithms. The application of the Wavelet transform as a feature for superficial track damage detection was also explored by [PERSON] [50].