## 5 Discussion  
In this paper, a new approach to detect rail corrugation from axle box accelerations is described. First, a section is identified based on the pressure level measured by the microphones. The section is then analysed by calculating the fast Fourier transform (FFT) of the axle box accelerations. The wavelength range of interest is observed, and the excitations found are further investigated by comparing runs at different vehicle speeds over the same section. This comparison allows the FFT contents to be divided into frequency-constant and wavelength-constant excitations. The spectrogram analysis distinguishes between the different sources of excitation: vehicle and rail modes of vibrations are identified as frequency-constant contents; bridges and switches are identified as impulsive effects that excite a wide range of frequencies within a very short time/distance span. Wheel and rail defects, both of which are constant in the wavelength domain, are distinguished based on the length of the response found in the acceleration data: wheel defects remain the  
Figure 13: (**a**) Axle box acceleration; (**b**) microphone measurement; (**c**) slip waves indicator; (**d**) ripple indicator. The section where the corrugation has been found is highlighted in yellow.  
same over the entire acquisition for many kilometres, while rail corrugations only affect the accelerations for a few hundred metres.  
The proposed methodology is then applied to data acquired on an in-service vehicle traveling on high-speed lines in Germany. The section of interest is identified using the microphone measurements by searching for a high value of the equivalent pressure level. The section is then analysed by computing the FFT: some wavelength-constant peaks are observed within the rail corrugation range. These peaks are then investigated in the spectrogram analysis to distinguish between wheel defects and rail corrugation based on the length of the excitations. One of the three peaks corresponds to an excitation that lasts for many kilometres and is therefore considered to be a potential wheel defect. The other two peaks, on the other hand, are only visible in the spectrogram for a few hundred metres and so the hypothesis of rail corrugation is formulated.  
The present work is considered a low technology readiness level (TRL) study to investigate the possibility to detect rail corrugation among many hundreds of kilometres using only measurements acquired on-board the vehicles. Visual and optical measurements could also be used to detect rail corrugation. However, it is important to acknowledge that the laser optical sensors are expensive and cannot be mounted on a complete fleet of vehicles. They are reliable, although they can be affected by environmental conditions such as dirtiness or light effects. On the contrary, accelerators and microphones can be easily installed on many vehicles, providing a large dataset of measurements. This allows both to continuously monitor the track and several vehicles to pass on the same track section. Comparing the acquisition from different vehicles on the same track section makes it possible to distinguish between the effects due to the vehicle component irregularities and to the rail irregularities.  
Further steps in the methodology validation are required before the implementation in real-world conditions. The automatisation of the methodology is an important aspect to be considered when implementing the methodology in practical settings. To this purpose the current manual steps should be simplified and adapted for an automatised implementation. These modifications can be performed without affecting the quality of the results achieved.