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Proceedings Paper

Fault diagnosis approach based on module fuzzy subsystems
Author(s): Hong Lv; Haiwen Yuan; Haibin Yuan
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Paper Abstract

Module fuzzy subsystems approach is introduced to solve Electrical Apparatus Control System (EACS) fault diagnosis problem in this paper. First, the input vectors are classified into several classifications using Radial Basis Function (RBF) neural network according to the faults occurring part. Then, a module subsystem is designed separately based on Fuzzy Neural Network (FNN) with exponential function fuzzy pattern matching. Finally, SF6 breaker faults diagnosis application is employed to validate the effectiveness of the proposed method. Simulation result shows that the diagnosis approach for the structure of module fuzzy subsystems can solute the problem of rules quick increasing with the input vector increasing, and the algorithm of fuzzy pattern matching with exponential function can improve the diagnosis precision.

Paper Details

Date Published: 6 November 2006
PDF: 6 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 635759 (6 November 2006); doi: 10.1117/12.717594
Show Author Affiliations
Hong Lv, BeiHang Univ. (China)
Haiwen Yuan, BeiHang Univ. (China)
Haibin Yuan, BeiHang Univ. (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)

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