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

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.

Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

2013 ◽  
Vol 579-580 ◽  
pp. 775-780
Author(s):  
Ying Ge Li ◽  
Gui Tang Wang ◽  
Zheng Li ◽  
Xin Liang Yin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Reconstruction Algorithm ◽  
Characteristic Parameter ◽  
Wavelet Neural Network ◽  
Voice Signal ◽  
Network Fault ◽  
Frequency Band Energy

Nicro-motor voice signal contains abundant running status information as well as vibration signal, aiming at the problem that it is difficult to obtain vibration signal in the production line of micro-motor, this paper proposes a micro-motor acoustic fault diagnosis methods based on loose wavelet neural network. Wavelet packet decomposition and reconstruction algorithm is utilized to extract micro-motor voice signals in each frequency band energy as the characteristic parameters of fault characteristic parameter samples will input to improve the BP neural network for training, build up the fault type of classifier, the realization of fault intelligent diagnosis. Application results show that a reasonable design of neural network has strong ability of fault identification; use loose micro-motor acoustic wavelet neural network fault diagnosis is feasible.

Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy

2013 ◽  
Vol 347-350 ◽  
pp. 371-375
Author(s):  
Xiang Yan Luo ◽  
Jun Bin Cao ◽  
Jun Qing Cao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Software Design ◽  
Oxygen Sensor ◽  
Wavelet Packet ◽  
Wavelet Neural Network ◽  
Wavelet Packet Analysis ◽  
Neural Network Method ◽  
Network Method

This paper focuses on airborne oxygen-making system shortcomings of oxygen sensor characteristic drift in, proposes a method of fault diagnosis. Oxygen sensor with a Wavelet packet analysis of feature extraction, based on wavelet neural network method to determine whether the sensor has failed, and sensor to detect hardware and software design are given.

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

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.

scholarly journals Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network

2018 ◽  
Vol 6 (3) ◽  
pp. 359-363
Author(s):  
Wenhui Teng ◽  
Shuxian Fan ◽  
Zheng Gong ◽  
Wen Jiang ◽  
Maofa Gong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Wavelet Neural Network

scholarly journals Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

2017 ◽  
Vol 7 (2) ◽  
pp. 158 ◽  
Author(s):  
Lifeng Wu ◽  
Beibei Yao ◽  
Zhen Peng ◽  
Yong Guan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Manifold Learning ◽  
Wavelet Neural Network ◽  
Roller Bearings
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