电力信息与通信技术2026，Vol.24Issue(3)：60-66,7.DOI:10.16543/j.2095-641x.electric.power.ict.2026.03.08
基于不平衡样本特征的电缆发热故障自动化诊断技术
Automated Diagnosis Technology for Cable Heating Faults Based on Unbalanced Sample Features
摘要
Abstract
In the automatic diagnosis of cable heating fault,the normal operation of cable data is much more than the fault data,resulting in the traditional fault diagnosis model is prone to bias the majority of classes(normal classes)while ignoring the minority of classes(fault classes)during training,which reduces the accuracy of fault identification.Therefore,an automatic fault diagnosis technique for cable heating based on unbalanced sample characteristics is proposed.By extracting the characteristic vector of cable heating fault,XGBoost decision tree is used to predict the unbalanced sample collection required for cable heating fault diagnosis.The BAgging-heterogeneous K-nearest neighbor integration algorithm is used to plan and classify the trained feature set,and a feature set classifier is established,and the automatic diagnosis of cable heating fault is realized according to the classification accuracy weight matrix.The experimental results show that the loss function of the proposed method rapidly converges and remains stable during the training process,which can accurately capture and judge the heating fault state of the cable.The F1 value obtained by the calculation is above 0.95,which further verifies the effectiveness and high precision of the method in the field of cable fault diagnosis.关键词
电缆发热故障/自动化诊断/不平衡样本特征/XGBoost决策树/卷积神经网络/特征集分类器/Loss函数Key words
cable heating fault/automatic diagnosis/unbalanced sample features/XGBoost decision tree/convolutional neural network/feature set classifier/loss function分类
信息技术与安全科学引用本文复制引用
杨锟..基于不平衡样本特征的电缆发热故障自动化诊断技术[J].电力信息与通信技术,2026,24(3):60-66,7.基金项目
国能包神铁路集团有限责任公司科研项目"基于物联网和数字孪生技术的10KV电缆路径位置、故障定位及状态实时监测系统研究"(BSKY-22-02). （BSKY-22-02）
