Enterprise information system automatic fault diagnosis and analysis method based on decision tree

Xin JIN; Longchuan YAN; Jun LIU; Shulin ZHANG

doi:10.11959/j.issn.1000-0801.2017058

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Enterprise information system automatic fault diagnosis and analysis method based on decision tree

Electric power information column | 更新时间：2024-06-05

- Enterprise information system automatic fault diagnosis and analysis method based on decision tree
- Telecommunications Science Vol. 33, Issue 3, Pages: 163-167(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017058
  CLC： TP319
- Published Online：2017-03，
  
  Published：20 March 2017
- 稿件说明：
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Xin JIN, Longchuan YAN, Jun LIU, et al. Enterprise information system automatic fault diagnosis and analysis method based on decision tree[J]. Telecommunications science, 2017, 33(3): 163-167.
DOI：

Xin JIN, Longchuan YAN, Jun LIU, et al. Enterprise information system automatic fault diagnosis and analysis method based on decision tree[J]. Telecommunications science, 2017, 33(3): 163-167. DOI： 10.11959/j.issn.1000-0801.2017058.

摘要

目前，大型企业信息系统规模和复杂度快速增长，但对故障的诊断分析仍主要依赖传统的人工经验，这不仅耗时、耗力，还影响对故障的及时处理。针对这一问题，创新性地提出了基于决策树的企业信息系统故障自动诊断分析方法，根据信息系统运行监控指标告警信息，实现对信息系统故障的自动诊断。利用某大型国有企业的实际生产运行数据，提取典型告警数据特征对该方法进行了验证，并在R语言环境下对决策树模型及其训练方法进行了仿真和对比分析。实验结果证明，该方法可以较为准确地实现故障自动快速诊断，有助于提高信息系统故障诊断分析效率。

Abstract

With the rapid growth of the scale and complexity of enterprise information systems

traditional fault diagnosis and analysis methods relying on human experiences and manual operations cost more and more labor and time. To solve this problem

an automatic algorithm was proposed. The algorithm exploits information from system operation monitoring indicators and alarm data

based on decision tree

to automatically diagnose and analyze faults of enterprise information systems. The algorithm was verified

and the decision tree model and training method was simulated and analyzed comparatively under R language environment

using alarm data extracted from real operation data of a typical large-scale enterprise system. The experiment results show that this algorithm is able to achieve fast automatic fault diagnosis accurately

and is much helpful on improving efficiencies of information system fault processing.

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references

曲朝阳，高宇峰，聂欣 . 基于决策树的网络故障诊断专家系统模型 [J ] . 计算机工程， 2008 , 34 ( 22 ): 215 - 217 .

QU Z Y , GAO Y F , NIE X . Network fault diagnosis expert system model based on decision tree [J ] . Computer Engineering , 2008 , 34 ( 22 ): 215 - 217 .

白建社，樊波，黄文华，等 . 基于决策树的变电站故障诊断知识表示与获取 [J ] . 电力系统及其自动化学报， 2004 , 16 ( 2 ): 5 - 8 .

BAI J S , FAN B , HUANG W H , et al . Knowledge representation and acquisition based on decision tree for substation fault diagnosis [J ] . Proceedings of the EPSA , 2004 , 16 ( 2 ): 5 - 8 .

黄勇，胡连城，刘增良 . 基于决策树算法的工业光纤网络通信故障检测系统 [J ] . 现代电子技术， 2014 ( 20 ): 134 - 136 .

HUANG Y , HU L C , LIU Z L . Industrial fiber network communication fault detection system based on decision tree algorithm [J ] . Modern Electronics Technique , 2014 ( 20 ): 134 - 136 .

车雅良，林初善，潘青，等 . ASON网络故障诊断系统推理机的设计与研究 [J ] . 广东通信技术， 2015 , 35 ( 3 ): 64 - 67 .

CHE Y L , LIN C S , PAN Q , et al . Design and research of ASON network fault diagnosis system inference engine [J ] . Guangdong Communication Technology , 2015 , 35 ( 3 ): 64 - 67 .

李智慧 . 大型网站技术架构：核心原理与案例分析 [M ] ．北京 : 电子工业出版社 , 2013 .

LI Z H . Large website technology architecture: core principles and case studies [M ] . Beijing : Publishing House of Electronics Industry , 2013 .

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