Dynamic Subdomain Pseudolabel Correction and Adaptation Framework for Multiscenario Mechanical Fault Diagnosis

Abstract

The subdomain adaptation (SA) based intelligent cross-domain fault diagnosis methods aim to reduce the conditional distribution shift caused by variable working conditions. However, existing SA methods may be limited by the quality of pseudolabels, since misclassified pseudolabels will lead to alignment between irrelevant subdomains, resulting in erroneous category-invariant knowledge being accumulated. To tackle this, we present a dynamic subdomain pseudolabel correction and adaptation (DSPC-A) framework. Specifically, we propose an end-to-end pseudolabel correction algorithm, which integrates an auxiliary network to learn clean and general target label distribution from noisy pseudolabels. So that, the auxiliary network can guide the SA model to perform precise subdomain alignment using learned label distribution. Moreover, to allow the synergy training of the additional auxiliary network and SA model, we introduce an iterative learning strategy to dynamically perform pseudolabel correction and subdomain alignment. The iterative training makes two models complement each other, thus achieving better SA ability and diagnosis performance. The DSPC-A framework has been thoroughly verified under three fault diagnostic scenarios: cross load, cross fault severity, and cross mechanical equipment. Case study results demonstrate the superiority of the DSPC-A, which improves the SA performance by solely implementing simple pseudolabel correction methods without other complex techniques.