Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (4): 34-40.doi: 10.13190/j.jbupt.2020-271

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A Hierarchical Category Embedding Based Approach for Fault Classification of Power ICT System

LI Jian-gui1, LIANG Yue1, GAO Peng-fei1, LIU Shao-hua2, MA Ying-long1   

  1. 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
  • Received:2020-12-21 Published:2021-07-13

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.

Key words: power information and communication technology customer service system, power text classification, hierarchical text classification, category embedding

CLC Number: