# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
## 8 Conclusions  
This paper aimed to identify the best options regarding the type of sensors and their location in a wayside system for wheel flat detection. Based on this, a multisensory layout scheme has been proposed to detect the presence of wheel flats on both passenger and freight trains. Hence, a wheel flat recognition methodology has been developed based on simulated signals obtained with a vehicle-track interaction model. Both shear  
Figure 17: Envelope spectrum analysis for 12 accelerometers (layout scheme 2) considering a defective and a healthy wheel of the Alfa Pendular due to the severity of the flat considering 10% noise. and acceleration measurement points were considered to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the evaluated shear and accelerations in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach with spectral kurtosis analysis. The influence of the type of sensors and installation location on the accuracy of the wheel flat detection system is analyzed. Two types of trains, namely the Alfa Pendular passenger train and a freight wagon, were considered. This study allowed us to draw the following conclusions:  
1. The methodology proposed in this work to detect wheel flats and distinguish defective from healthy wheels is based on the envelope spectrum analysis. In this regard, extracting the impulsive signal with amplitude modulation proved a critical preprocessing step before performing the envelope spectrum. In this study, the center frequency and the bandwidth frequency of the signal were obtained first, and then this information was used as input to detect defective wheels through the envelope spectrum approach. 2. In situations where the signal is significantly contaminated by noise, the use of a layout scheme composed of accelerometers is clearly more advantageous than using the strain gauges to perform an envelope spectrum to detect defective wheels. When the envelope spectrum is performed with the acceleration as input, there are significant differences in the amplitudes of the envelope spectrum response obtained in a scenario with healthy wheels and in a scenario with a defective one. 3. This finding confirms the importance of kurtosis to obtain the center frequency because the impulse signal obtained with the accelerometer is more obvious than that obtained with the strain gauge. Moreover, in noised contaminated signals, the impulse signal is even more visible due to the defective wheel, and with kurtosis, the center frequency and bandwidth would be obtained more accurately. 4. To detect a defective wheel, there is no need to install 12 strain gauges or accelerometers along the perimeter of a wheel since, according to the results obtained in this work, it is clear that the same results are achieved with only two accelerometers on the rail web. 5. The results show that the system, regardless of the position of the sensors and the severity of the flat, is always effective in detecting wheel flats, which is a major advantage regarding the installing process. For the final development of the proposed methodology, it is imperative to develop an automatic indicator to distinguish a defective wheel from a healthy one. Moreover, based on artificial intelligence techniques, a threshold should be defined to detect a damaged wheel. Finally, the application of the proposed method will be studied considering different speeds and unevenness profiles and tested under real field conditions by considering environmental disturbance, measurement error, and electronic interference. The optimization of the system should be thoroughly evaluated with respect to the various characteristics of wheel flats and several types of trains. Conceptualization, A.M.; methodology, A.M.; software, P.A.M., A.M.; validation, A.M., P.A.M.; formal analysis, P.A.C., R.C.