Title :
Fault diagnosis of turbo-generator based on RBF neural networks
Author :
Li, Rui-xin ; Wang, Dong-feng ; Han, Pu ; Zhang, Jun
Author_Institution :
Coll. of Mech. Eng., Tianjin Univ., China
Abstract :
In this paper, the structure and working principle of Radial Basis Function (RBF) Neural Network (NN) are analyzed. A new method for constructing and training of parallel RBF-NN is proposed. A compound heuristic Genetic Algorithm (GA) based on Singular Value Decomposition (SVD) is introduced for structure training of RBF-NN. Based on the advanced strategies proposed above, RBF-NN is used for fault diagnosis of turbo-generator. Computer simulation experimental results show that the approach is effective.
Keywords :
digital simulation; fault diagnosis; genetic algorithms; learning (artificial intelligence); radial basis function networks; singular value decomposition; turbogenerators; GA; SVD; computer simulation; fault diagnosis; genetic algorithm; parallel RBF-NN training; radial basis function neural networks; singular value decomposition; turbogenerator; Biological neural networks; Fault detection; Fault diagnosis; Feedforward neural networks; Genetic algorithms; Humans; Neural networks; Shape; Singular value decomposition; Vectors;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1260116
