# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 5 Conclusions  
In this study, an anomaly detection method on the wheel flat problem is proposed. In previous studies, wheel flat is diagnosed using only signal processing techniques without a deep learning approach. This could only detect the presence or absence of a wheel flat. However, in this paper, a deep learning approach is applied to anomaly detection with varying conditions of size and number of wheel flats. At first, the data was pre-processed to be suitable for deep learning through signal processing. Then, anomaly detection was performed using the deep learning technique. The normality and anomaly were classified according to the safety standards of urban railway vehicles. The deep learning model of the LeNet-5 was applied and elaborately modified to observe the performance in a supervised manner for anomaly detection. Different pooling methods and activation functions were applied to improve both the performance and the speed of this domain data. First, a vibration signal data according to the presence or absence of a wheel flat was obtained using SIMPACK, a multi-body dynamic software. The noise signals measured from Seoul Metro Line 7 were overlapped with the flat signals obtained from the simulations. Then, two signal processing methods were used to create data suitable for deep learning. The wheel of railway vehicles is a rotating device, and its running speed changes frequently during actual operation. Accordingly, as the frequency changes according to the rotation speed, order analysis was performed to compensate for this point. Then, in order to utilize the CNN algorithm during deep learning, a Spectrogram image was created through STFT. Second, deep learning was performed after changing the LeNet-5 model in the CNN algorithms to improve performance. For the data used in the LeNet model, the size of the spectrogram image was changed to \(32\times 32\). The deep learning results showed an accuracy of 97%. In addition, the AUC value obtained through the ROC curve, which is often used in the binary classification model, was confirmed. The AUC value was 0.9688, which was also high. Therefore, the anomaly detection technique of wheel flat defects through the LeNet model generated an excellent performance. This study also checked whether the deep learning result was excellent when pre-processing was additionally performed. For this purpose, the raw data (Time domain) was compared with the pre-processing data using the signal processing method. As a result of deep learning, the case with pre-processing was about 24% higher in accuracy, and the AUC value was also about 0.21 higher. The reason is because of noise signal overlapping in the flat signal in the raw data (time-domain) when the wheel flat size was small. It was well postulated that a study of the anomaly detection for wheel flats with a modified LeNet model would be very meaningful to cultivate further research with deep learning. In addition, it is expected that the following effects will be achieved. First, it is possible to secure the passenger safety and ride comfort of railway vehicles. Second, it is possible to reduce maintenance costs. Third, railway maintenance staff can focus on more important parts of maintenance. This is expected to increase the efficiency of maintenance. Finally, conducting the tests in real conditions, in the case of rail vehicles, is not so easy in a university laboratory due to safety and cost issues. Because of these reasons, the real flat signal data could not be applied to deep learning. This has been left for future study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Accuracy** & **AUC** \\ \hline STFT & 97 & 0.9688 \\ Raw Data (Time-domain) & 73.26 & 0.7544 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of deep learning results of STFT and raw data. Nevertheless, this study would become a useful reference for anomaly detection research on rail vehicles using deep learning.