Adaptive radial basis function network and its application in turbine-generator vibration fault diagnosis

PowerCon 2002 - 2002 International Conference on Power System Technology, Proceedings

Volumn 3, Issue , 2002, Pages 1607-1610

  Wan, Shuting ^a   Li, Heming ^a   Li, Yonggang ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

fault diagnosis; generator; radial basis function network (RBFN); rotor; stator; vibration

Indexed keywords

CLUSTERING ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; FAILURE ANALYSIS; FAULT DETECTION; FUNCTIONS; ITERATIVE METHODS; PLASMA DIAGNOSTICS; ROTORS; ROTORS (WINDINGS); STATORS; TURBOGENERATORS; WINDING;

ADAPTIVE RADIAL BASIS FUNCTION NETWORK; CONVENTIONAL ALGORITHMS; GENERATOR; ITERATION CLUSTER ALGORITHM; RADIAL BASIS FUNCTION NETWORKS (RBFN); STATOR WINDING FAULTS; VIBRATION; VIBRATION FAULT DIAGNOSIS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84969750834     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPST.2002.1067804     Document Type: Conference Paper

References (4)

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3. Li Weiqing, Wang Shaoyu, Examination Analysis and Prevention of Generator Fault, Beijing: China Electric Power Press, 1996, pp.287-302.
4. Zhang Zhihua, Zheng Nanning, Zheng Haibing, "A Maximum-Entropy Learning Algorithm of Radial Basis Function (RBF) Neural Networks," Chinese J. Computers, vol.24, no.5, pp.474-479,May 2001.