Contrastive Learning Enhanced Transfer Framework for Fault Diagnosis of Aerospace Electromechanical Systems

Fault diagnosis of aerospace electromechanical systems in orbit faces the dual challenges of data scarcity and domain discrepancy. To address this problem, a contrastive learning enhanced transfer framework is proposed. Large-scale general bearing data are employed for self-supervised pretraining to obtain transferable representations, which are then adapted with a limited number of labeled CMG samples. The model is subsequently validated on in-orbit CMG data under real operating conditions. Experimental results demonstrate that the proposed method substantially improves diagnostic performance compared with traditional approaches, achieving a relative gain of about 24.1 %. These findings highlight the effectiveness of integrating contrastive learning with transfer learning to overcome small-sample and crossdomain challenges, and provide a feasible pathway for in-orbit health management of aerospace electromechanical systems.