Wavelet neural network based fault diagnosis of asynchronous motor

In order to better solve asynchronous motor complex fault characteristics, improve the reliability of the diagnosis and accuracy, combined with wavelet transform technique, construct a wavelet neural network, wavelet transform technology feature extraction asynchronous motor as a signal wavelet neural network's input vector, and the wavelet neural network algorithm was used to optimize, realize the motor identify types of fault, through the simulation experiment data diagnosis results show that this method is effective and feasible. Based on the wavelet analysis and neural network fault diagnosis method of research.

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Asynchronous Motor Fault Diagnosis Based on Wavelet Neural Network

2009 International Conference on Information Engineering and Computer Science ◽

10.1109/iciecs.2009.5363667 ◽

2009 ◽

Cited By ~ 1

Author(s):

Guizhen Zhou ◽

Guorong Liu ◽

Yiping Luo

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Asynchronous Motor ◽

Wavelet Neural Network

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Fault diagnosis of piston compressor based on Wavelet Neural Network and Genetic Algorithm

2008 7th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2008.4592852 ◽

2008 ◽

Cited By ~ 2

Author(s):

Li Jinru ◽

Liu Yibing ◽

Yan Keguo

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Piston Compressor ◽

Wavelet Neural Network

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Fault Diagnosis of HVDC Transmission System Using Wavelet Energy Entropy and the Wavelet Neural Network

2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe) ◽

10.23919/epe20ecceeurope43536.2020.9215964 ◽

2020 ◽

Author(s):

Cuicui Liu ◽

Feng Wang ◽

Fang Zhuo ◽

Ziqian Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Transmission System ◽

Wavelet Neural Network ◽

Hvdc Transmission ◽

Wavelet Energy ◽

Energy Entropy ◽

Wavelet Energy Entropy

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Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.382.163 ◽

2011 ◽

Vol 382 ◽

pp. 163-166

Author(s):

Qing Xin Zhang ◽

Jin Li ◽

Hai Bin Li ◽

Chong Liu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Fundamental Frequency ◽

Wavelet Packet ◽

Frequency Component ◽

Wavelet Neural Network ◽

Simulation Software ◽

Monitoring Methods ◽

Stator Current ◽

Fundamental Frequency Component

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

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WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1048 ◽

2013 ◽

Vol 427-429 ◽

pp. 1048-1051

Author(s):

Xu Sheng Gan ◽

Hao Lin Cui ◽

Ya Rong Wu

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Convergence Rate ◽

Analog Circuit ◽

Particle Swarm ◽

Pso Algorithm ◽

Wavelet Neural Network ◽

Swarm Optimization ◽

Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

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