Deep Contrastive BiGRU Network for Cross-Domain Aero-Engine Fault Detection

Abstract

Cross-domain aero-engine fault detection is challenging because vibration signals collected under different operating conditions exhibit substantial distribution shifts. Existing diagnostic models mainly focus on feature extraction or global domain alignment, often overlooking fault class separability near decision boundaries. This study proposes a perturbation-enhanced deep contrastive learning bidirectional gated recurrent unit framework (DCL-BiGRU) for unsupervised cross-domain fault detection. A shared BiGRU encoder captures bidirectional temporal dependencies, while deep contrastive learning improves intra-class compactness and inter-class separation. A controlled perturbation strategy strengthens robustness to noise and feature variation, and maximum classifier discrepancy guides uncertain target samples toward reliable source-supported class regions. Experiments on two laboratory datasets and one practical aero-engine dataset show accuracies of 95.62% and 92.48% for two transfer tasks, with a mean accuracy of 94.05%. The method outperforms DCL, BiGRU, TCA, JDA, MNN, and DANN approaches, confirming improved domain alignment, class discrimination, and diagnostic robustness under practical conditions.