针对化工行业多变量时间序列数据的复杂性及隐私保护需求，提出了一种基于时域注意力门控无源自适应算法（temporal attention gated source-free adaptation, TAGSFA），显著提升了跨域故障诊断的性能。该方法通过融合门控Transformer网络、嵌入式离散化技术与邻域聚类策略，有效捕捉源域与目标域间的时序相关性，避免了负迁移效应。在田纳西伊斯曼数据集和连续搅拌釜式反应器数据集上的实验表明，TAGSFA在不依赖源域数据的情况下，优于现有方法，显著提高了诊断精度和鲁棒性。消融实验验证了时域注意力和邻域聚类对模型性能的关键作用。

To address the complexity of multivariate time series data in the chemical industry and privacy protection requirements, TAGSFA (temporal attention gated source-free adaptation algorithm was proposed, significantly improving the performance of crossdomain fault diagnosis. The temporal correlations between source and target domains were effectively captured and negative transfer effects were avoided through the integration of gated Transformer networks, embedded discretization techniques, and neighborhood clustering strategies. The experimental results on both TEP (Tennessee Eastman process) dataset and CSTR (continuous stirred tank reactor) dataset have demonstrated that TAGSFA outperforms existing methods in diagnostic accuracy and robustness without source domain data access. The critical roles of time-domain attention and neighborhood clustering have been validated through ablation studies.