| 基于层次化类别嵌入的电力ICT系统故障分类 |
| |
| 引用本文： | 李建桂,梁越,高鹏飞,刘绍华,马应龙. 基于层次化类别嵌入的电力ICT系统故障分类[J]. 北京邮电大学学报, 2021, 44(4): 34-40. DOI: 10.13190/j.jbupt.2020-271 |
| |
| 作者姓名： | 李建桂 梁越 高鹏飞 刘绍华 马应龙 |
| |
| 作者单位： | 1. 华北电力大学 控制与计算机工程学院, 北京 102206;2. 北京邮电大学 电子工程学院, 北京 100876 |
| |
| 基金项目： | 国家重点研发计划项目（2018YFC0831404；2018YFC0830605） |
| |
| 摘 要： | 为解决电力信息通信客服系统在故障研判时存在故障分类准确率低甚至误分的问题，提出基于层次化类别嵌入的文本分类方法，进行电力信息通信系统故障识别.首先，基于电力信息通信系统故障的用户保修工单文本数据构建电力信息通信系统层次化电力故障标签；其次，提出了基于层次化深层金字塔卷积神经网络和基于层次化中断循环神经网络2种层次化文本分类方法，采用层次化类别嵌入方法逐层进行故障类型分类.实验结果表明，基于层次化深层金字塔卷积神经网络的方法效果最优，可以提供高效、准确的故障识别服务.
|
| 关 键 词： | 电力信息通信客服系统 电力文本分类 层次化文本分类 类别嵌入 |
| 收稿时间： | 2020-12-21 |
A Hierarchical Category Embedding Based Approach for Fault Classification of Power ICT System |
| |
| LI Jian-gui,LIANG Yue,GAO Peng-fei,LIU Shao-hua,MA Ying-long. A Hierarchical Category Embedding Based Approach for Fault Classification of Power ICT System[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 34-40. DOI: 10.13190/j.jbupt.2020-271 |
| |
| Authors: | LI Jian-gui LIANG Yue GAO Peng-fei LIU Shao-hua MA Ying-long |
| |
| Affiliation: | 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China |
| |
| Abstract: | To solve the low classification accuracy oreven misclassification issue in fault diagnosis, a text classification method based on hierarchical category embedding is proposed in information and communication technology (ICT) customer service systems. First, a hierarchical label system is constructed for the failure data in power ICT systems based on the textual data of the work orders.Then, hierarchical deep pyramid convolutional neural networks (HDPCNN) and hierarchical disconnected recurrent neural networks are proposed, which adopt hierarchical category embedding technique for level-by-level fault type classification. The experimental results show that the hierarchical text classification algorithm HDPCNN has the best classification accuracy, which can provide efficient and accurate services for fault type recognition. |
| |
| Keywords: | power information and communication technology customer service system power text classification hierarchical text classification category embedding |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《北京邮电大学学报》浏览原始摘要信息 |
|
点击此处可从《北京邮电大学学报》下载全文 |
