中国邮电高校学报(英文) ›› 2008, Vol. 15 ›› Issue (1): 68-74.doi: 1005-8885 (2008) 01-0068-07

• Artificial Intelligence • 上一篇    下一篇

Research on intelligent fault diagnosis based on time series analysis algorithm

陈刚;刘洋;周文安;宋俊德   

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2007-06-11 修回日期:1900-01-01 出版日期:2008-03-31
  • 通讯作者: 陈刚

Research on intelligent fault diagnosis based on time series analysis algorithm

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2007-06-11 Revised:1900-01-01 Online:2008-03-31
  • Contact: Chen Gang

摘要:

Aiming to realize fast and accurate fault diagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent fault diagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio (GLR) test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced. Secondly, the intelligent fault diagnosis model is established by introducing neural network technology, and thereby, the anomaly information of each node in end-to-end network is integrated and processed in parallel to intelligently diagnose the fault cause. Finally, server backup solution in enterprise information network is taken as the simulation scenario. The results demonstrate that the proposed method can not only detect fault occurrence in time, but can also implement online diagnosis for fault cause, and thus, real-time and intelligent fault management process is achieved.

关键词:

network;management,;fault;diagnosis,;time;series;analysis,;neural;network

Abstract:

Aiming to realize fast and accurate fault diagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent fault diagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio (GLR) test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced. Secondly, the intelligent fault diagnosis model is established by introducing neural network technology, and thereby, the anomaly information of each node in end-to-end network is integrated and processed in parallel to intelligently diagnose the fault cause. Finally, server backup solution in enterprise information network is taken as the simulation scenario. The results demonstrate that the proposed method can not only detect fault occurrence in time, but can also implement online diagnosis for fault cause, and thus, real-time and intelligent fault management process is achieved.

Key words:

network management;fault diagnosis;time series analysis;neural network

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