Share Email Print
cover

Proceedings Paper

Fault diagnosis of electric apparatus component using improved RBFNN
Author(s): Haibin Yuan; Haiwen Yuan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Radial basis function neural network (RBFNN) approach is investigated and applied for fault diagnosis of electric apparatus control system under working state, the aim is to achieve accurate fault type identification when component failures. After fault occurrence relationships among fault types, fault feature and fault cause are analyzed, non-linear mapping relationship between fault feature and fault cause is extracted based on engineering viewpoint, in which 5 significant measure parameters is treated as network input, and 11 typical fault type is treated as output. In order to reduce training time and accelerate convergence speed, K-mean clustering and adaptive learning method is adopted to improve RBF neural network performance. Simulation and test result is shown, and comparison between RBF network and BP network is also discussed to validate the method.

Paper Details

Date Published: 30 October 2006
PDF: 6 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63582G (30 October 2006); doi: 10.1117/12.717951
Show Author Affiliations
Haibin Yuan, Beihang Univ. (China)
Haiwen Yuan, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

© SPIE. Terms of Use
Back to Top