Feb. 13, 2024, 5:42 a.m. | Chao Wang Zhuo Chen Ziyan Zhang Chiyi Li Kai Song

cs.LG updates on arXiv.org arxiv.org

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 …

apply capabilities challenge cs.cl cs.lg data graphs knowledge knowledge graphs machine machine learning machine learning techniques maintenance novel paper power prediction predictive solve tools transformer transformers

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