@Article{sdhm.2025.069811,
AUTHOR = {Mingxing Wu, Chengzhen Li, Xinyan Feng, Fei Chen, Yingchun Feng, Huihui Song, Wenyu Wang, Faye Zhang},
TITLE = {Memory-Fused Dual-Stream Fault Diagnosis Network Based on Transformer Vibration Signals},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {19},
YEAR = {2025},
NUMBER = {6},
PAGES = {1473--1487},
URL = {http://www.techscience.com/sdhm/v19n6/64511},
ISSN = {1930-2991},
ABSTRACT = {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.},
DOI = {10.32604/sdhm.2025.069811}
}