  In this paper, we address the challenge of learning with limited fault data
for power transformers. Traditional operation and maintenance tools lack
effective predictive capabilities for potential faults. The scarcity of
extensive fault data makes it difficult to apply machine learning techniques
effectively. To solve this problem, we propose a novel approach that leverages
the knowledge graph (KG) technology in combination with gradient boosting
decision trees (GBDT). This method is designed to efficiently learn from a
small set of high-dimensional data, integrating various factors influencing
transformer faults and historical operational data. Our approach enables
accurate safe state assessments and fault analyses of power transformers
despite the limited fault characteristic data. Experimental results demonstrate
that this method outperforms other learning approaches in prediction accuracy,
such as artificial neural networks (ANN) and logistic regression (LR).
Furthermore, it offers significant improvements in progressiveness,
practicality, and potential for widespread application.
