针对变压器故障诊断中的数据分布差异，提出加权域适应方法提升跨设备诊断准确率。
Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis
- 融合MMD与CORAL，并根据分布差异动态加权特征
- 在真实数据集上比微调提升7.9%，比MMD-CORAL提升2.2%
- 适用于小样本和多类型变压器的鲁棒诊断场景
保障电力变压器可靠运行对电网稳定至关重要。溶解气体分析（DGA）广泛用于故障诊断，但传统方法依赖启发式规则，易导致结果不一致。基于机器学习的方法虽提升了诊断精度，但变压器在不同工况下运行，其类型、环境及操作条件差异造成诊断数据分布偏移，直接迁移模型常失效，亟需域适应技术。为此，本文提出一种特征加权的域适应方法（MCW），结合最大均值差异（MMD）与相关性对齐（CORAL），并利用柯尔莫哥洛夫-斯米尔诺夫（K-S）统计量为各特征分配可调节权重，优先对分布差异大的特征进行对齐，从而增强源域与目标域的一致性。在变压器数据集上的实验表明，该方法相比微调提升7.9%，相比MMD-CORAL（MC）提升2.2%，且在不同训练样本量下均表现更优，验证了其鲁棒性。
原文摘要 · Abstract (English)
Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely on heuristic rules, which may lead to inconsistent results. Machine learning (ML)-based approaches have improved diagnostic accuracy; however, power transformers operate under varying conditions, and differences in transformer type, environmental factors, and operational settings create distribution shifts in diagnostic data. Consequently, direct model transfer between transformers often fails, making techniques for domain adaptation a necessity. To tackle this issue, this work proposes a feature-weighted domain adaptation technique that combines Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) with feature-specific weighting (MCW). Kolmogorov-Smirnov (K-S) statistics are used to assign adaptable weights, prioritizing features with larger distributional discrepancies and thereby improving source and target domain alignment. Experimental evaluations on datasets for power transformers demonstrate the effectiveness of the proposed method, which achieves a 7.9% improvement over Fine-Tuning and a 2.2% improvement over MMD-CORAL (MC). Furthermore, it outperforms both techniques across various training sample sizes, confirming its robustness for domain adaptation.
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