Guo Yu, Ju Guangshuo, Zhang Jundong
Marine Engineering College, Dalian Maritime University, Dalian, 116000, China.
Faculty of Natural, Mathematical and Engineering Sciences, King's College London, London, WC2R 2LS, England, UK.
Sci Rep. 2024 Oct 26;14(1):25447. doi: 10.1038/s41598-024-75088-8.
Traditional models for Imbalanced Fault Diagnosis (IFD) face challenges in practical applications due to domain shifts caused by varying working conditions and machinery. Domain Generalization (DG) models provide an advantage over traditional approaches by learning class-discriminative and domain-invariant feature representations, allowing them to generalize to unseen target data. However, the scarcity of fault samples relative to healthy ones limits their application in real-world industrial scenarios. In this paper, we propose a Domain Mixed-Enhanced Domain Generalization Network (DEMDGN) that enhances IFD performance by utilizing mixup-based data augmentation and domain-based discrepancy metrics to align feature distributions across multiple heterogeneous source domains. By creating domain-invariant features, DEMDGN allows robust fault diagnosis under varying conditions. Extensive experiments on one marine machinery dataset and two bearing datasets demonstrate that the proposed method effectively addresses class imbalance and domain shift problems, achieving superior diagnostic performance.
传统的不平衡故障诊断（IFD）模型在实际应用中面临挑战，因为工作条件和机械的变化会导致领域转移。领域泛化（DG）模型通过学习类判别和领域不变的特征表示，比传统方法具有优势，使它们能够推广到未见的目标数据。然而，故障样本相对于健康样本的稀缺限制了它们在实际工业场景中的应用。在本文中，我们提出了一种领域混合增强领域泛化网络（DEMDGN），该网络通过利用基于混合的数据增强和基于领域的差异度量来对齐多个异构源领域的特征分布，从而提高IFD性能。通过创建领域不变特征，DEMDGN允许在不同条件下进行鲁棒的故障诊断。在一个船舶机械数据集和两个轴承数据集上进行的大量实验表明，该方法有效地解决了类不平衡和领域转移问题，实现了卓越的诊断性能。
