Wavelet Integrated CNN With Dynamic Frequency Aggregation for High-Speed Train Wheel Wear Prediction

Huan Wang, Yan-Fu Li, Tianli Men

Abstract: The wheel wear status of high-speed trains (HSTs) is an essential indicator of their safety and reliability. However, due to the time-varying operating state of HSTs, noisy and complex non-stationary signals are collected. This makes it difficult for data-driven algorithms to learn valuable discriminative features from data. Therefore, this inspired us to introduce signal analysis methods with clear physical meaning to improve the interpretability and performance of prediction models. This paper proposes a novel multi-layer wavelet integrated convolutional neural network (MWI-Net) for predicting HST wheel-wear. Specifically, discrete wavelet transform (DWT) extends the feature learning space of CNN from the time domain to the wavelet domain, thereby capturing the frequency features that are difficult to learn in the time domain. As a remarkable information space, the DWT can effectively alleviate the frequency aliasing problem, enabling MWI-Net to distinguish valuable frequency information from complex signals. In particular, the proposed dynamic frequency aggregation mechanism endows MWI-Net with excellent frequency analysis and feature selection capabilities. Experiments on the real operation dataset of CRH1A HSTs show that MWI-Net accurately predicts the wheel wear curves, which is more competitive than existing deep learning methods. Furthermore, we demonstrate the feature learning mechanism inside MWI-Net through visual analysis and illustrate how it optimizes and extracts valuable features layer by layer.