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1 2025

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Reinforcement-enhanced deep transfer fusion network for multi-domain transformer fault diagnosis via acoustic signatures

Mu Mingliang, Niu Jianfei, Guo Guangqian, Tian Maoxiang, Li Jinlin, Li Jingyi

Diagnostyka

2025

Vol. 26, No. 3

art. no. 2025303

During operation, power transformers generate continuous vibrational signatures bearing mechanical faultinduced impulses, which constitute the fundamental evidence for equipment condition assessment. To investigate acoustic-fingerprint characteristics under varying operating conditions, this paper proposes a Reinforced Integrated Deep Transfer Learning Network (REDTLN) for multi-fault-domain diagnostics. The methodology first constructs multiple specialized Deep Transfer Learning Networks (DTLNs) using novel kernel Maximum Mean Discrepancy (kMMD) variants, enabling source-specific adaptation to enrich transferable feature representations. Subsequently, a unified unsupervised ensemble framework integrates multi-metric divergence measures, employing a reinforcement-guided combinatorial search algorithm to discover optimal DTLN integration rules. This intelligent fusion mechanism significantly enhances multisource transfer capability, improving diagnostic accuracy and robustness in dynamic noise environments and complex operational scenarios. Experimental results confirm the model's efficacy in precisely identifying abnormal states while maintaining sustained >95% accuracy for representative faults under diverse acousticinterference conditions.