DocumentCode :
620536
Title :
Research of motor fault diagnosis based on PSO algorithm
Author :
Wei Hu ; Gui Liu ; Li Fu ; Hongmei Zhang
Author_Institution :
Fac. of Aerosp. Eng., Shenyang Aerosp. Univ., Shenyang, China
fYear :
2013
fDate :
25-27 May 2013
Firstpage :
4600
Lastpage :
4603
Abstract :
Aiming at improving the convergence performance of conventional BP neural network, this paper presents a PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The way of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity is smallest to mutate its velocity according to some probability. The method could avoid the particle sticking to the local minimum effectively, also the new method could improve the convergence ability of BP network. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to motor rotor broken fault diagnosis.
Keywords :
backpropagation; fault diagnosis; induction motors; machine control; particle swarm optimisation; BP network; PSO algorithm; convergence performance; gradient descent method; induction motors; iteration loop; motor rotor broken fault diagnosis; particle swarm optimization; Accuracy; Convergence; Fault diagnosis; Induction motors; Neural networks; Particle swarm optimization; Rotors; Fault Diagnosis; Motor; Neural Network; PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
Type :
conf
DOI :
10.1109/CCDC.2013.6561765
Filename :
6561765
Link To Document :
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