Research On Fault Diagnosis Method Of Wireless Sensor Network Node

With the continuous development of radio technology,power electronics technology and artificial intelligence technology,the stability,reliability and durability of wireless sensors are also constantly improving.In recent years,wireless sensor network(WSN.)technology has gradually matured and can operate stably in complex and harsh environmen,ts,grradually replacing the sensor network of the traditional bus mode.However,as the structure of wireless sensor networks becomes more and more complicated,and the influenc.e of environmental factors,the probabili ty of occurrence of sensor node failures is also increasing,which may affect the performance of the entire network,and even lead to network paralysis.Therefore,for the characteristics of wireless sensor networks,it is very important to study sensor node faults and fault diagnosis.In this context,this paper studies the fault diagnosis method of wireless sensor network nodes.Firstly,this paper classifies the common faults of wireless sensor network nodes,which are mainly divided into no fault,sensor module fault,power module fault,communication module fault and process.or module fault,and establish the correlation between fault type and fault symptom.Secondly,the design and im plementation of the hardware and software of the data acquisition system platform are carried out.The data acquisition system is mainly composed of sensor nodes,aggregation nodes and control platforms.Its main function is to collect relevant symptom data of different fault types,so as to provide data for subsequent algorithm simulation experiments.Then the paper studies the application of the kernel extreme learning machine(KELM)algorithm in WSN node fault diagnosis,and improves it.The simulation experiment of the improved algorithm is carried out by Matlab platform,and compared with the traditional single-core KELM algorithm.The results show that The improved KELM algorithm has a higher accuracy of fault diagnosis.Finally,the improved KELM algorithm is fur ther optimized,and a fault diagnosis algorithm based on RS-PSO-KELM is proposed.The algorithm uses rough sets to reduce decision attributes and extracts the simplest fault diagnosis rules.Then,the kernel extreme learning machine model is established,and the KELM kernel parameters are optimized by the particle swarm optimization algorithm.Finally,the optimal kernel extreme learning m,achine model is used to c.lassify the input sample data to realize fault diagnosis.The accuracy of the RS-PSO-KELM method proposed in this paper is compared with that of different KELM algorithms.The experimental analysis is carried out under the condition that the sample data has higher and lower confidence.The results show that the algorithm has high accuracy and strong anti-interference ability,which can meet the design requirements.