A Class Alignment Multisource Domain Adaptation for Partial Discharge Condition Assessment With Unknown Faults in GIS

作者：Yanxin Wang, Jing Yan, Zhou Yang, Wenjie Zhang, Jianhua Wang, Yingsan Geng, Dipti Srinivasan · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3543239 · 被引用次数：19 · 研究领域：Water Systems and Optimization、Geophysical Methods and Applications、Infrastructure Maintenance and Monitoring

Recently, domain adaptation has emerged as a powerful technique for on-site partial discharge (PD) condition assessment in gas-insulated switchgear (GIS). However, most existing methods face three major challenges: 1) relying on a single source domain for model development poses difficulties in effectively utilizing source domain samples with distribution differences; 2) limited condition assessment for unknown fault samples on-site, which faces distributional differences between multiple source domains; and 3) handling only a single task, which makes it challenging to generalize to multiple tasks simultaneously. To address these concerns, we propose a class alignment multisource domain adaptation network (CLMSDAN) for GIS PD condition assessment with unknown faults. First, a diversity feature extractor is developed to extract diverse features while addressing the negative transfer issue caused by knowledge differences by mining both interdomain and intradomain features, thus enabling the transfer of rich knowledge at multiple levels. Second, a novel multisource domain adaptation approach is employed from multiple perspectives to align distribution and distinguish between shared and unknown classes. Finally, a multiclassifier complementary strategy is introduced to recognize unknown faults, which automatically filters out source domain irrelevant class samples while distinguishing the contributions of different source domains to the target task. Experimental results show that...