Physically Interpretable Wavelet-Guided Networks With Dynamic Frequency Decomposition for Machine Intelligence Fault Prediction

Abstract

Machine intelligence fault prediction (MIFP) is crucial for ensuring complex systems' safe and reliable operation. While deep learning has become the mainstream tool for MIFP due to its excellent learning abilities, its interpretability is limited, and it struggles to learn frequencies, making it challenging to understand the physical knowledge of signals at the frequency level. Therefore, this article proposes a physically interpretable wavelet-guided network (WaveGNet) with deep frequency separation for MIFP, inspired by the sound theoretical basis and physical meaning of discrete wavelet transform (DWT). WaveGNet expands the feature learning space of CNN into the frequency domain, allowing for a better understanding of the physical insights behind the frequency level. Specifically, WaveGNet involves a derivable and learnable frequency learning layer (FL-Layer) consisting of a wavelet-driven frequency decomposition module and a convolution-driven feature learning module. Multiple DWT-driven FL-Layers are used in WaveGNet to achieve deep frequency decomposition and multiresolution frequency feature learning in a coarse-to-fine manner. The effectiveness of WaveGNet was evaluated in real high-speed train wheel wear monitoring and high-speed aviation bearing fault diagnosis cases. Experimental results showed that WaveGNet outperforms cutting-edge deep learning algorithms and has excellent fault diagnosis and prediction abilities. Furthermore, an in-depth analysis of the learning mechanism of wavelet-driven CNN from the frequency domain perspective was conducted.