Bearing fault diagnosis using Radial Basis Function network and adaptive neuro fuzzy classifier

Author(s):  
Rohit Tiwari ◽  
P.K. Kankar ◽  
V.K. Gupta
2013 ◽  
Vol 302 ◽  
pp. 474-480
Author(s):  
Huo Ching Sun ◽  
Chao Ming Huang ◽  
Yann Chang Huang ◽  
Hsing Feng Chen

A particle swarm optimization-based radial basis function network (PSO-RBFN) is presented to diagnose vibration faults of steam turbine-generator sets (STGS) in a power plant. The proposed PSO algorithm is used to automatically tune the control parameters of the RBFN. The test results demonstrate that the proposed PSO-RBFN has a higher diagnostic accuracy than the RBFN and multilayer perceptron network (MLPN) trained by error back-propagation algorithm. Moreover, this paper has demonstrated that the proposed PSO-RBFN can be as a reliable tool for vibration fault diagnosis of STGS.


2016 ◽  
Author(s):  
Olímpio Murilo Capeli ◽  
Euvaldo Ferreira Cabral Junior ◽  
Sadao Isotani ◽  
Antonio Roberto Pereira Leite de Albuquerque

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