Open Access

ARTICLE

Memory-Fused Dual-Stream Fault Diagnosis Network Based on Transformer Vibration Signals

Mingxing Wu¹, Chengzhen Li¹, Xinyan Feng¹, Fei Chen², Yingchun Feng¹, Huihui Song¹, Wenyu Wang³, Faye Zhang^3,*

1 State Grid Shandong Electric Power Company Ultra High Voltage Company, Jinan, 250061, China
2 State Grid Shandong Electric Power Company, Jinan, 250061, China
3 School of Control Science and Engineering, Shandong University, Jinan, 250061, China

* Corresponding Author: Faye Zhang. Email:

Structural Durability & Health Monitoring 2025, 19(6), 1473-1487. https://doi.org/10.32604/sdhm.2025.069811

Received 01 July 2025; Accepted 04 September 2025; Issue published 17 November 2025

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.

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Keywords

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