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A Fault Diagnosis Method of WSN Wireless Communication Module Based on Fuzzy Neural Network

A wireless communication module and fuzzy neural network technology, applied in the field of information perception and recognition, can solve problems such as networks that are not suitable for topology dynamic changes, and achieve improved reliability and practicability, fast convergence speed, and high accuracy of fault diagnosis Effect

Active Publication Date: 2020-04-24
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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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  • A Fault Diagnosis Method of WSN Wireless Communication Module Based on Fuzzy Neural Network
  • A Fault Diagnosis Method of WSN Wireless Communication Module Based on Fuzzy Neural Network
  • A Fault Diagnosis Method of WSN Wireless Communication Module Based on Fuzzy Neural Network

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Embodiment Construction

[0049] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0050] The present invention focuses on the analysis of sensor signals for most of the fault diagnosis methods, and there are few studies on the fault location and analysis of the WSN node itself. According to the voltage and current parameters related to the fault characteristics, the concept of active in the circuit and the direction of inflow and outflow, the abnormality of the current or voltage of a module will affect the state of the parameters of other modules on the entire series circuit, and the abnormality of voltage or current The diagnosis can finally determine the fault location and cause. Establish a fuzzy neural network current model for fault diagnosis of wireless communication modules. Firstly, fuzzy rules are adaptively extracted by subtractive clustering, and then the rule parameters are optimized and adjusted by a hybrid learning method combining...

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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 identification, and in particular relates to a fault diagnosis method for a WSN wireless communication module based on a fuzzy neural network. Background technique [0002] WSN failure refers to the abnormality of one or several parts of the system, which causes it to lose its original function or its performance cannot meet the design requirements. Due to the difference in the WSN operating environment, WSN node hardware and external interference types, the fault types and manifestations of WSN are also different. According to the WSN system function and realization function, its failure can be divided into node failure and network failure. WSN nodes are divided into sensor nodes and sink nodes, so node faults are also divided into sensor node faults and sink node faults. A network failure refers to a problem in the network communication protocol or collaborative management t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/04H04W84/18G06N3/08
CPCG06N3/08H04W24/04H04W84/18
Inventor 薛善良周奚韦青燕朱世照
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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