Abstract: In some wireless sensor network(WSN) security monitoring systems,the nodes transfer large amounts of data in a long time,which causes the phenomenon of power decreasing and power amplifier(PA) being burned in the wireless data transceiver unit,but this kind of fault diagnosis method is generally complex and inefficient.In order to solve these problems,based on the analysis of WSN cell-level fault diagnosis,this paper proposed a fault diagnosis method based on fuzzy neural network by using the current model of wireless data transceiver unit.Firstly,according to the relationship between the emission current,the temperature and the supply voltage,the current model is established.Then,the fuzzy neural network model structure is determined by the clustering algorithm,and the hybrid learning algorithm is used to optimize the front and rear parameters of fuzzy rules.Finally,the fuzzy neural network parameters are extracted to establish the WSN node fault diagnosis model.The experimental results show that the presented fault diagnosis method of wireless data transceiver unit possesses low computational complexity and high diagnostic accuracy.Compared with Gaussian process regression model,the computational complexity of this method is reduced by 22.4%,and the diagnostic accuracy is increased by 17.5%.

Key words: Wireless sensor network,Fault diagnosis,Current model,Fuzzy neural network