WSN wireless communication module fault diagnosis method based on fuzzy neural network

Abstract

The invention discloses a WSN wireless communication module fault diagnosis method based on a fuzzy neural network. A fuzzy neural network current model is established by using emission consumption parameters corresponding to a DHT11 temperature and humidity sensor under different temperatures and voltages for the fault diagnosis of a wireless communication module. For data subjected to normalization processing, firstly an initial structure and parameters of the fuzzy neural network are adaptively determined by using subtraction clustering, then parameter optimization and adjustment are carried out on the model by using a hybrid learning method combining the particle swarm optimization algorithm with the least square method, and finally fault diagnosis is carried out on a test sample by using a trained diagnosis model. According to the WSN wireless communication module fault diagnosis method disclosed by the invention, the advantages of fuzzy reasoning and the neural network are integrated, an improved learning algorithm is adopted, the fuzzy neural network current model of the wireless communication module is established for the relation among the current, the voltage and the faults of a WSN, and the model is short in training time, high in convergence speed and high in fault diagnosis efficiency.

Description

Technical field [0001] The invention belongs to the technical field of information perception and recognition, and specifically relates to a fault diagnosis method of a WSN wireless communication module based on a fuzzy neural network. Background technique [0002] WSN failure refers to a situation in which one or several parts of the system are abnormal, causing it to lose its original function or its performance does not meet the design requirements. Due to the different types of WSN operating environment, WSN node hardware and external interference, WSN fault types and manifestations are also different. According to WSN system functions and implementation functions, its faults can be divided into two types: node faults and network faults. WSN nodes are divided into sensor nodes and sink nodes, so node failures are also divided into sensor node failures and sink node failures. Network failure refers to a large-scale failure caused by a problem in the network communication pro...

AI Technical Summary

Problems solved by technology

[0004] In 2001, Chessa S and Santi P proposed a comparison-based fault diagnosis algorithm, which realizes fault diagnosis based on the test results between nodes, but this fault diagnosis is not suitable for networks with dynamically changing topology

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