Reinforcement-enhanced deep transfer fusion network for multi-domain transformer fault diagnosis via acoustic signatures
1
Binzhou Power Supply Company, State Grid Shandong Electric Power Company
Submission date: 2025-01-16
Final revision date: 2025-06-11
Acceptance date: 2025-06-18
Online publication date: 2025-07-02
Publication date: 2025-07-02
Corresponding author
KEYWORDS
power transformerensemble learningdeep transfer learningvoiceprint recognitiontarget domain adaptation
TOPICS
ABSTRACT
During operation, power transformers generate continuous vibrational signatures bearing mechanical fault-induced 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 multi-source 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 acoustic-interference conditions.
FUNDING
This study was supported by Project Supported by the State Grid Shandong Electric Power Company Technology Project Funding (520615240007).
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