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.

翻译：本文针对电力变压器故障数据有限情况下的学习挑战展开研究。传统运维工具缺乏对潜在故障的有效预测能力，而海量故障数据的稀缺使得机器学习技术难以有效应用。为解决该问题，我们提出一种融合知识图谱（KG）技术与梯度提升决策树（GBDT）的新方法。该方法专为从小规模高维数据中高效学习而设计，整合了影响变压器故障的多因素及历史运行数据，能够在故障特征数据受限的情况下实现电力变压器安全状态的准确评估与故障分析。实验结果表明，该方法在预测精度上优于人工神经网络（ANN）、逻辑回归（LR）等其他学习方法，同时在先进性、实用性和广泛推广应用潜力方面具有显著提升。