Rotating machinery fault diagnosis based on one-dimensional signals often suffers from performance degradation due to distribution shifts caused by varying operational conditions. Domain generalization (DG) methods aim to learn a model from labeled source domains that can generalize to unseen target domains. However, existing DG approaches typically face limitations in effectively simulating inter-domain shifts, imposing fine-grained consistency constraints, and handling ambiguous decision boundaries. To address these challenges, this paper proposes a novel DG framework. First, a Distribution-Aware Source Domain Feature Augmentation module is introduced, which perturbs channel-wise statistics of features to generate augmented samples that preserve semantics while exhibiting distributional diversity, thereby simulating potential domain variations. Second, an Uncertainty-Guided Feature Alignment Regularization is designed to dynamically weight the alignment loss based on feature uncertainty and the discrepancy between original-augmented feature pairs, enforcing robust feature consistency. Third, an Adaptive Angular Margin Loss is incorporated to enhance discriminative power by constructing adaptive decision boundaries in the angular space based on intra-class compactness and inter-class separation. Extensive experiments on the PHM2009 gearbox dataset and a proprietary bearing dataset under a leave-one-domain-out protocol demonstrate the superiority of the proposed method. It achieves average accuracies of 80.46% and 87.46% on the two cases, respectively, significantly outperforming state-of-the-art comparative methods. The ablation study further confirms the contribution of each core component.