DocumentCode :
3353964
Title :
Hydraulic Turbines Vibration Fault Diagnosis by RBF Neural Network Based on Particle Swarm Optimization
Author :
Jia Rong ; Zhang Xin-wei ; Chen Xiao-yun ; Li Hui ; Liu Jun ; Song Xin-fu
Author_Institution :
Inst. of Water Resources & Hydro-Electr. Eng., Xi´an Univ. of Technol., Xi´an
fYear :
2009
fDate :
27-31 March 2009
Firstpage :
1
Lastpage :
4
Abstract :
For the system of vibration faults diagnosis of hydraulic turbines, the deficiency of generalization ability using single BP Network is analyzed and a radial basis function (RBF) neural network algorithm based on particle swarm optimization (PSO) is presented. It has advantage of being easy to realize, simple operation and profound intelligence background. The parameters and connection weight are optimized by the algorithm. The diagnostic results of the instance show that it has better classifying results, higher precision, faster convergence and it provides a new way in the field of fault diagnosis of hydraulic turbines.
Keywords :
fault diagnosis; hydraulic turbines; neural nets; particle swarm optimisation; power engineering computing; vibrations; RBF neural network; hydraulic turbines vibration fault diagnosis; particle swarm optimization; radial basis function; Acceleration; Fault diagnosis; Feedforward neural networks; Function approximation; Genetic algorithms; Hydraulic turbines; Neural networks; Particle swarm optimization; Particle tracking; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-2486-3
Electronic_ISBN :
978-1-4244-2487-0
Type :
conf
DOI :
10.1109/APPEEC.2009.4918409
Filename :
4918409
Link To Document :
بازگشت