Fault Diagnosis Based On Rough Neural Network Wsn Nodes

Wireless Sensor Networks (WSN) is a current emerging and hot technique, its emergence changes the way of human beings interact with nature. WSN has high research value and broad application prospects in the military and civil ,and many other areas. However, as the increased of degree of automation in WSN, its structure has become more complex, and the WSN mainly work on the complex conditions and harsh environments, the nodes of WSN have to bear the wind, sun, rain and many other negative factors, it is prone to failure, so the original design features can not be complete. Moreover, the enviromental conditions in the monitored region in which the nodes of WSN were deployed are similar, most likely the majority of nodes simultaneously fail, and resulting in paralysis of the entire network of WSN. Therefore, it is extremly necessary to monitor the working status of the nodes in WSN in the real-time, the timely and accurate fault diagnosis of nodes in WSN can effectively improve the WSN operation reliability and safety, and ensure that WSN complete the scheduled tasks.In this paper, firstly, studied the characteristics, types, levels of nodes'fault in WSN, and then researched the basis of individual characteristics of the Rough Sets theory and neural network algorithms in depth, study the possibility and the way of integrating the Rough Sets theory and Neural Network algorithms. Based on the characteristics of node in WSN, select the BP neural network to integrated with Rough Sets, because BP has the inherent defects as easy to fall into partial minimal and slow convergence, this paper proposed a new improved AMSABP algorithm, for the condition of the input attribute value of fault monitor system is contineous, this paper proposed the integrated RS-AMSABP fault diagnosis method. Firstly, this method get the most simple decision-making table of the fault diagnosis by the improved discriminate matrix, then established diagnosis rules by the table. Finally, constructed the AMSABP network model by the diagnosis rules, and trainning the network through the sample data. The expriment results of fault diagnosis of the node in WSN show that RS-AMSABP algrithom made the high diagnostic accurate to 99.74% and low calculate time compared with other diagnosis method.Because WSN mainly work in the complex and bad enviroment, when the failure occurred in WSN, the input attribute value of fault monitor system is likely contineous, this paper proposed constructed the rough neuron by the two endpoints of the interval numbers of the input attribute of the fault monitor system, and applied rough decision-making analysis method constructed a decision information system of WSN fault diagnosis with the interval numbers, so the problem of the fault diagnosis of nodes in WSN with the interval numbers can be resolved by the the three-layers feed-forward rough neural network with the interval numbers. The simulation results show the diagnosis algrithom based on the Interval-Numbers Rough Neural Network improved the diagnostic accurate to 99.57% when the computing time was greatly reduced.This paper proposed a whole solution scheme for the fault diagnosi of nodes in WSN, effectively meet the actual needs which the developing of WSN technology and application. It has high practical value.