2018 Vol.1 Jun No.3

• Title: Wireless network control system fault detection process simulation
• Name: Robin Scott
• Company: Concordia University,Portland
• Abstract:

  Wireless network control system failure rate is high, the diagnosis is difficult, wireless network transmission signal to a large extent can effectively response fault categories; In order to effectively for wireless network control system fault detection, this paper proposes a wireless network transmission signal fault detection method of nonlinear time sequence of correlation dimensions, the key to improve the traditional correlation dimension extraction algorithm research; 4 kinds of wireless network control system for acquisition of fault state of transmission signal analysis of signal processing, extracting the correlation characteristic, through improved the correlation algorithm, fault feature extraction, and improved the precision of the calculation of correlation dimension, network fault condition improved algorithm of the standard deviation reduced by more than 15% to 30% than the traditional algorithm, clustering distribution increase obviously, embodies the fault detection of superior performance; Fault detection results show that, according to improve the correlation dimension of feature extraction method can effectively detect the wireless network control system failure, the accurate rate increased by 21.4%, has great practical application value.

• Keyword: wireless network; nonlinear time series; correlation dimension; fault detection;
• DOI: 10.12250/jpciams2018030114
• Citation form: Robin Scott.Wireless network control system fault detection process simulation[J]. Computer Informatization and Mechanical System, 2018, vol. 1, pp. 30-34.

[1]Wang Zhongcai, Li Yongbi. Research on intrusion detection system based on data mining [J]. Science and technology bulletin, 2012, 28(8): 150-152.
[2]Bing Zheng, Peng Jianhua. Nonlinear Dynamics [M]. Beijing: Higher Education Press,2003.
[3]Zhang Yi, Sheng Huiping, Hu Guangbo. Compressor fault diagnosis based on phase space reconstruction and K-L transform[J].Compressor Technology，2011(4)：19-21.
[4]Lu Keda, Wan Li, Wu Jieming. Research on network security event prediction based on data mining technology [J] Science bulletin, 2012, 28(6): 37-40
[5]Guhathakurta Kousik, Bhattacharya Basabi, Chowdhury A. Roy. Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes[J]. Physica A: Statistical Mechanics and its Applications, 2010, 389(9): 1874-1882.