Unsupervised Domain Adaptation (UDA) has achieved tremendous success in the task of solving new unlabeled target domains by leveraging its knowledge learned from labeled source datasets and has been widely utilized in wind turbine fault diagnosis. However, in the actual industrial scenarios, data privacy, commercial confidentiality, and transmission efficiency constraints make the source domain data inaccessible. In addition, internal structured information modeling and multi-structured fusion of wind turbine data under non-stationary operating conditions is not sufficiently taken into account. To solve the aforementioned problems, a privacy-preserving source-free unsupervised multiscale graph domain adaptation network (SFUGDA) is proposed. Specifically, the proposed SFUGDA consists of two main stages, the pre-training stage of the source diagnostic model and the adaptation stage of the target domain. In the pre-training stage, a novel multi-scale multi-structured network with hybrid attention mechanism is implemented, which can effectively fuse deep and shallow features to extract multi-scale node information. Meanwhile, the node information is further fused with the topology information to capture robust, structured, and discriminative feature. In the domain adaptation stage, we consider novel loss functions and constrain the target domain through neighborhood clustering, regularization, and predictive diversity for self-training to achieve high-precision fusion and clustering, obtaining a diagnostic model for the final target domain. To verify its effectiveness and superiority, we evaluate SFUGDA in a variety of experiments including comparison and ablation experiments about gears and bearings of the wind turbine under variable operating conditions, especially time-varying operating conditions. Experimental results indicate that SFUGDA yields state-of-the-art results among multiple advanced comparison methods.

中文翻译：

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SFUGDA：用于海上风电机组跨域故障诊断的无源无监督多尺度图域自适应网络，具有隐私保护

无监督域适应 （UDA） 利用其从标记源数据集中学到的知识，在解决新的未标记目标域的任务中取得了巨大成功，并已广泛用于风力涡轮机故障诊断。然而，在实际工业场景中，数据隐私、商业机密和传输效率的限制使得源域数据无法访问。此外，在非平稳运行条件下，内部结构化信息建模和风力涡轮机数据的多结构融合没有得到充分考虑。针对上述问题，该文提出一种隐私保护无源无监督多尺度图域自适应网络（SFUGDA）。具体来说，所提出的 SFUGDA 包括两个主要阶段，即源诊断模型的预训练阶段和目标域的适应阶段。在预训练阶段，实现了一种具有混合注意力机制的新型多尺度多结构网络，可以有效地融合深浅特征，提取多尺度节点信息。同时，将节点信息与拓扑信息进一步融合，以捕获鲁棒性、结构化和判别性特征。在域适应阶段，我们考虑新的损失函数，并通过邻域聚类、正则化和预测多样性对目标域进行约束进行自我训练，以实现高精度的融合和聚类，获得最终目标域的诊断模型。 为了验证其有效性和优越性，我们在各种实验中评估了 SFUGDA，包括对风力涡轮机齿轮和轴承在可变运行条件下，特别是时变运行条件下的比较和烧蚀实验。实验结果表明，SFUGDA 在多种高级比较方法中产生了最先进的结果。