Fault detection for network control system based on equally divided sampling period

2009 ◽  
Vol 29 (5) ◽  
pp. 1248-1250
Author(s):  
Jie ZHANG ◽  
Yu-ming BO
2013 ◽  
Vol 846-847 ◽  
pp. 778-781
Author(s):  
Hong Liang Guo ◽  
Shao Ying Kong

Wireless network control system is characterized by high uncertain delay time, thus the state of the system can not be fully observed. The fault characteristic is interfered by time delay so to be unstable, leading inaccurate fault detection. Traditional fault detection method of wireless network control system is usually based on the characteristics of the stability of the network status data. If the network has time delay fluctuations, it is unable to obtain accurate fault detection results. This paper presents a stochastic delay fault detection method. It builds a state space model of the system, analyzes the delay vector between the sensor end of the system and the controller end, depending on the delay residual signal of the system and the corresponding evaluation function to obtain the system failure detection result. The final simulation result shows that this method has high accuracy in the detection of Stability and randomness of the wireless network fault detection. Thus it is an effective wireless network control system fault detection method.


2013 ◽  
Vol 846-847 ◽  
pp. 782-785
Author(s):  
Shuang Ye

Wireless network control system has high failure rate, and is difficult to be diagnosed. Wireless network transmission signal effectively reflect the failure categories. In order to effectively detect the wireless network control system fault, this paper presents a fault detection method of correlation dimensional nonlinear timing characteristics for wireless network transmission signal, which mainly improves the traditional correlation dimension extraction algorithm. The method processes and analyzes the collected transmission signal of four types wireless network control system in fault condition, and then extract fault feature through an improved correlation dimension algorithm. It improves the calculation accuracy of the correlation dimension with a standard deviation 15% -30% than that of the traditional algorithm, and it significantly enhances the clustering distribution characteristics, reflecting its superiority in fault detection. Fault detection results show that the improved feature extraction method for correlation dimension can effectively detect failure in wireless network control system, whose accuracy is improved by 21.4%, and has great practical value.


Author(s):  
A. K. Kanaev ◽  
◽  
A. N. Gorbach ◽  
E. V. Oparin, ◽  
◽  
...  

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