MC-WDWCNN: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis
Jianyu Zhou, Xiangfeng Zhang, Hong Jiang, Zhenfa Shao, Benchi Ma, Rong Zhou
Abstract
Abstract Deep learning-based methods have shown promising results in fault diagnosis, but research on interpretability and noise robustness still needs to be done. A multi-channel wide-kernel wavelet convolutional neural network is proposed to address these issues. Firstly, a first layer of multi-channel wide-kernel convolution is designed to fuse different weight information and suppress high-frequency noise. Secondly, a discrete wavelet transform block is designed to retain the low-frequency components of the discrete wavelet transform for signal denoising and feature dimension reduction. At the same time, Improved Balance Dynamic Adaptive Threshold is used to enhance the robustness of the model’s noise and the sparsity of features, making the model easier to optimize. Lastly, a power spectrum and normalized class activation mapping are designed to validate the post-hoc explanations of the model. The effectiveness and reliability of the Multi-Channel Wide Kernel Wavelet Convolutional Neural Network are verified through two gearbox datasets.