Optimization of a DBN-based fault diagnosis algorithm for Oil-immersed transformers
This work proposes an optimized Deep Belief Network architecture that outperforms traditional DBNs by approximately 5% and matches the diagnostic precision of state-of-the-art architectures (e.g., Transformers), while offering superior computational efficiency and robustness under limited data conditions.