Prior knowledge-based residuals shrinkage prototype networks for cross-domain fault diagnosis

Abstract

In engineering practice, device failure samples are limited in the case of unexpected catastrophic faults, thereby limiting the application of deep learning in fault diagnosis. In this study, we propose a prior knowledge-based residual shrinkage prototype network to resolve the fault diagnosis challenges under limited labeled samples. First, our method combines general supervised learning and metric meta-learning to extract prior knowledge from the labeled source data by utilizing a denoised residual shrinkage network. Further, the knowledge extracted from the supervised learning is used for prototype metric training to achieve a better feature representation for the fault diagnosis. Finally, our approach outperforms a series of baseline methods in the few-shot cross-domain diagnostic task on the gearbox and bearing datasets. A diagnosis accuracy of more than 95% has been achieved in a variety of working conditions for diagnostic tasks, which is far higher than the existing basic method.