A receptive field transfer strategy via layer-aligned distillation learning for fault signal denoising

To improve fault diagnosis performance in complex noise environments, effective signal denoising techniques are necessary. However, traditional denoising methods have proven inadequate for multivariate fault signal denoising, neglecting the correlation among these signals. To this end, we propose a novel denoising module, inspired by traditional signal decomposition and reconstruction methods. Furthermore, to enhance the performance of proposed denoising module, we consider the influence of the receptive field and develop a receptive field transfer strategy using layer-aligned distillation learning. The experiments demonstrate that our approach effectively balances the denoising performance and computational load, offering a novel strategy for developing high-performance denoising networks. What's more, our strategy reduces the difficulty for fault diagnosis tasks under complex noise environments.