Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2009
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
Online Fault Detection, Induction Motors, Independent Component Analysis, Fuzzy neural network
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 8th International Conference on Advances in Power System Control, Operation and Management. London, United Kingdom, 2009 November 8-11
Identifier
10.1049/cp.2009.1841
Publisher
Institute of Engineering and Technology
City or Country
London, UK
Citation
WANG, Zhaoxia; CHANG, C. S.; GERMAN, X.; and TAN, W.W..
Online fault detection of induction motors using independent component analysis and fuzzy neural network. (2009). Proceedings of the 8th International Conference on Advances in Power System Control, Operation and Management. London, United Kingdom, 2009 November 8-11.
Available at: https://ink.library.smu.edu.sg/sis_research/6867
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.