DocumentCode :
3244065
Title :
Online fault detection of induction motors using independent component analysis and fuzzy neural network
Author :
Wang, Zhao-Xia ; Chang, C.S. ; German, X. ; Tan, Woei Wan
Author_Institution :
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260
fYear :
2009
fDate :
8-11 Nov. 2009
Firstpage :
1
Lastpage :
6
Abstract :
This paper proposes the use of independent component analysis and fuzzy neural network for online fault detection of induction motors. The most dominating components of the stator currents measured from laboratory motors are directly identified by an improved method of independent component analysis, which are then used to obtain signatures of the stator current with different faults. The signatures are used to train a fuzzy neural network for detecting induction-motor problems such as broken rotor bars and bearing fault. Using signals collected from laboratory motors, the robustness of the proposed method for online fault detection is demonstrated for various motor load conditions.
Keywords :
Fuzzy neural network; Independent Component Analysis; Induction Motors; Online Fault Detection;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Advances in Power System Control, Operation and Management (APSCOM 2009), 8th International Conference on
Conference_Location :
Hong Kong, China
Type :
conf
DOI :
10.1049/cp.2009.1841
Filename :
5526671
Link To Document :
بازگشت