A data mining method based on unsupervised learning and spatiotemporal analysis for sheath current monitoring

Wang Y.; Ye H.; Zhang T.; Zhang H.

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A data mining method based on unsupervised learning and spatiotemporal analysis for sheath current monitoring

机译：一种基于无监督学习和鞘电流监测的时空分析的数据挖掘方法

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摘要

Sheath current is one of the key indicators of underground power cable conditions. Considering the limitations of existing model-based methods for sheath current monitoring and difficulty in handling the increasing amount of unlabeled sheath current data accumulated by cable monitoring systems, we propose a data mining method based on unsupervised learning and spatiotemporal analysis of sheath currents for underground power cable monitoring. Tests based on real historical data demonstrate that the proposed method can effectively reveal unknown inherent patterns in unlabeled sheath current data. (C) 2019 Published by Elsevier B.V.

机译：鞘电流是地下电力电缆条件的关键指标之一。考虑到基于模型的鞘电流监测方法的局限性和难以处理电缆监测系统累计累积的未标记鞘当前数据的难度，我们提出了一种基于无监督学习和天空分析的地下电流的数据挖掘方法电缆监控。基于真实历史数据的测试表明，所提出的方法可以有效地揭示未标记的鞘当前数据中的未知固有模式。（c）2019年由elestvier b.v发布。

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来源
《Neurocomputing》 |2019年第4期|54-63|共10页
作者
Wang Y.; Ye H.; Zhang T.; Zhang H.;
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作者单位

Tsinghua Univ Dept Automat Beijing 100084 Peoples R China;

Tsinghua Univ Dept Automat Beijing 100084 Peoples R China;

China Property & Casualty Reinsurance Co Ltd Beijing 100033 Peoples R China;

Zhengzhou Huali Informat Technol Co Ltd Zhengzhou 450000 Henan Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Sheath current monitoring; Data mining; Unsupervised learning; Spatiotemporal analysis;

机译：鞘当前监测;数据挖掘;无监督学习;时尚分析;

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专利

1. A data mining method based on unsupervised learning and spatiotemporal analysis for sheath current monitoring [J] . Wang Y., Ye H., Zhang T., Neurocomputing . 2019,第AUGa4期

机译：基于无监督学习和时空分析的护套电流监测数据挖掘方法
2. Unsupervised Topographic Learning for Spatiotemporal Data Mining [J] . Guenael Cabanes, Younes Bennani Advances in Artificial Intelligence . 2010,第MAC期

机译：时空数据挖掘的无监督地形学习
3. Unsupervised Topographic Learning for Spatiotemporal Data Mining [J] . Guéna?lCabanes, YounèsBennani Advances in Artificial Intelligence . 2010,第1期

机译：时空数据挖掘的无监督地形学习
4. Unsupervised learning based mining of academic data sets for students’ performance analysis [C] . Liana Maria Crivei, Gabriela Czibula, George Ciubotariu, IEEE International Symposium on Applied Computational Intelligence and Informatics . 2020

机译：基于无监督学习的学术数据集挖掘，用于学生的绩效分析
5. Unsupervised data mining methods for functional data analysis and feature selection. [D] . Rattakorn, Panaya. 2009

机译：用于功能数据分析和特征选择的无监督数据挖掘方法。
6. A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring [O] . Mingzhen Li, Jianming Liu, Tao Zhu, 2019

机译：通过护套电流监测的无监督学习改进的基于行波的新方法
7. Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases [O] . Marie-laure Reinberger, Peter Spyns, Walter Daelemans, 2003

机译：Lexon的挖掘：应用无监督学习方法创建本体基础

A data mining method based on unsupervised learning and spatiotemporal analysis for sheath current monitoring

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