TY  - EJOU
AU  - Wu, Mingxing 
AU  - Li, Chengzhen 
AU  - Feng, Xinyan 
AU  - Chen, Fei 
AU  - Feng, Yingchun 
AU  - Song, Huihui 
AU  - Wang, Wenyu 
AU  - Zhang, Faye 

TI  - Memory-Fused Dual-Stream Fault Diagnosis Network Based on Transformer Vibration Signals
T2  - Structural Durability \& Health Monitoring

PY  - 2025
VL  - 19
IS  - 6
SN  - 1930-2991

AB  - As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an exponential moving average (EMA) strategy to ensure the smooth evolution of prototypes over time; the domain memory bank is periodically updated and clusters potential noisy features, dynamically tracking domain shift trends, thereby optimizing the decoupled feature learning process. Experimental validation was conducted on a ±110 kV transformer vibration testing platform using typical fault types including winding looseness, core looseness, and compound faults. The results show that the proposed method achieves a fault diagnosis accuracy of 99.2%, providing a highly generalizable solution for the intelligent operation and maintenance of power equipment.
KW  - Power transformer; partial domain; multi-source domain; intelligent fault diagnosis

DO  - 10.32604/sdhm.2025.069811