An adaptive fused domain-cycling variational generative adversarial network for machine fault diagnosis under data scarcity

Data synthesis is reshaping the way that artificial intelligence tackles the challenge of machine fault diagnosis under data scarcity in the practical industry. Effectively fusing highly realistic synthetic data with multi-source scarce real-world data to enhance model performance is a critical and pressing need. Existing data synthesis methods are limited by the insufficient exploitation of rich multi-domain information and the difficulty of achieving high-quality fusion between synthetic and real data, which ultimately constrains diagnostic performance. Therefore, an adaptive fused domain-cycling variational generative adversarial network (AFDVGAN) is proposed. Firstly, a smooth-regularized variational framework is constructed to stabilize latent space representation, enhancing the structural consistency of synthetic data and improving training stability. Secondly, a ratio-controlled domain-cycling mechanism is established to dynamically coordinate feature transfer across spatial, time-frequency, and frequency domains, thereby strengthening multi-domain feature modeling and improving data synthesis quality. Finally, a multi-metric guided adaptive data fusion strategy is designed to lead to high-quality fusion of synthetic and real data based on statistical and time-frequency metrics, providing robust data support to enhance the decision-making accuracy of the diagnostic model. For the case studies involving electric locomotive bearings and high-speed aerospace bearings, comparisons with typical and state-of-the-art methods show that AFDVGAN generates higher-quality synthetic data. After data fusion, the diagnostic accuracies reach 99.81% for the locomotive case and 99.16% for the aerospace bearing case. These results verify the effectiveness and advantages of AFDVGAN for fault diagnosis in engineering scenarios under data scarcity.