A Domain Adaptation Method for Fault Diagnosis of Planetary Gearboxes Under Varying Operating Conditions with Time–Frequency Enhanced Attention

Deep learning-based methods have achieved promising results in planetary gearbox fault diagnosis. However, complex vibration signals often contain redundant information and disturbance-related responses, and varying operating conditions can cause distribution discrepancy between training and testing data, leading to degraded diagnostic performance. To address this coupled challenge, a diagnostic method termed DART18 (Domain Adaptation diagnosis of ResNet18 embedded with a Time–frequency enhanced attention mechanism) is proposed. DART18 is designed to improve both the discriminability and transferability of fault features by combining input-level time–frequency refinement with feature-level distribution alignment. Specifically, vibration signals are first transformed by the optimal generalized S-Transform (OGST) into time–frequency representations to characterize their joint time–frequency information. Then, TFEAM is designed to refine the input time–frequency representations before deep feature extraction. By aggregating features from different receptive fields and adaptively emphasizing fault-related time–frequency structures, TFEAM provides more informative inputs for subsequent feature learning. On this basis, ResNet18 is employed to extract fault features, and multi-kernel maximum mean discrepancy (MK-MMD) is introduced to statistically align the feature distributions of the source and target domains by jointly using labeled source-domain data and unlabeled target-domain data. Experimental results on two planetary gearbox datasets under multiple domain adaptation tasks show that DART18 consistently outperforms five comparative methods in terms of accuracy and F1-score, demonstrating its effectiveness and robustness for fault diagnosis under varying operating conditions.