# Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis

arXiv: https://arxiv.org/abs/2505.14896 · HTML: https://mlsanity.com/paper/2505.14896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 4 of 4)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Domain Adaptive Neural Networks for Object Recognition](https://mlsanity.com/paper/1409.6041/) (2014)
- [Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN](https://mlsanity.com/paper/2206.12076/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Strength-Monotonic Law for Domain Alignment in Frozen-Embedding Bioacoustic Classification](https://mlsanity.com/paper/2610.09737/) (2026)

## Abstract (arXiv)

Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely on heuristic rules, which may lead to inconsistent results. Machine learning (ML)-based approaches have improved diagnostic accuracy; however, power transformers operate under varying conditions, and differences in transformer type, environmental factors, and operational settings create distribution shifts in diagnostic data. Consequently, direct model transfer between transformers often fails, making techniques for domain adaptation a necessity. To tackle this issue, this work proposes a feature-weighted domain adaptation technique that combines Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) with feature-specific weighting (MCW). Kolmogorov-Smirnov (K-S) statistics are used to assign adaptable weights, prioritizing features with larger distributional discrepancies and thereby improving source and target domain alignment. Experimental evaluations on datasets for power transformers demonstrate the effectiveness of the proposed method, which achieves a 7.9% improvement over Fine-Tuning and a 2.2% improvement over MMD-CORAL (MC). Furthermore, it outperforms both techniques across various training sample sizes, confirming its robustness for domain adaptation.