Wavelet-based neural network model for track stiffness signal detection

Abstract

With the rapid development of the railway industry, railway safety has received increasing attention. However, traditional methods for signal detection are limited by high cost and energy requirements. Data-driven methods are becoming increasingly popular for railway signal detection. In this paper, we propose a wavelet-based network model for railway track stiffness signal detection by combining deep neural networks and wavelet transform. In the training phase, we propose a wavelet-based convolutional neural network. We use wavelet coefficients to enhance the input features to improve the convolutional neural network. In the detection phase, we combine the sliding window algorithm and the voting algorithm to detect anomalous signals. Extensive experiments on general metrics demonstrate the effectiveness of our proposed model. The classification performance still outperforms the general network by 30–50% in terms of accuracy, precision and F1 score, which is a huge improvement. In addition, we test the model classification performance under different wavelet functions to validate the superiority of neural networks using wavelets.