Blending-Target Domain Adaptation for Intelligent Fault Recognition With Minimum Cycle Spiking Encoding and Adversarial Attack

Abstract

The generalization performance of intelligent fault recognition models is tied to the assumption of identical distribution. Domain adaptation allows the source model to be extended to single or multitarget domains with distribution shifts. However, the reliable transfer of multitarget domain adaptation (MTDA) is inseparable from domain annotation. In this article, we consider a more pragmatic but challenging MTDA setting where domain labels are absent. This setting threatens most existing methods due to the elusive gaps and agnostic affiliation. We propose a systematic approach to the new setting. First, the category semantic destruction and self-supervised clustering are used to estimate domain labels. Second, the attack features are constructed to consolidate adaptation by gradient alignment and classifier robustness. Extensive experiments demonstrate that the new setting is quite challenging for existing methods, while the proposed method outperforms the existing methods and effectively suppresses transfer preference.