A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

Nadjarpoorsiyahkaly, Mahnoosh and Lim, Chee Peng 2011, A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors, in BIC-TA 2011 : Proceedings of the 6th International Conference on Bio-Inspired Computing: Theories and Applications, IEEE Computer Society, Los Alamitos, Calif., pp. 146-150.

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Title A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors
Author(s) Nadjarpoorsiyahkaly, Mahnoosh
Lim, Chee Peng
Conference name Bio-Inspired Computing : Theories and Applications. Conference (6th : 2011 : Penang, Malaysia)
Conference location Penang, Malaysia
Conference dates 27-29 Sept. 2011
Title of proceedings BIC-TA 2011 : Proceedings of the 6th International Conference on Bio-Inspired Computing: Theories and Applications
Editor(s) [Unknown]
Publication date 2011
Conference series Bio-Inspired Computing : Theories and Applications. Conference
Start page 146
End page 150
Total pages 5
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) autoencoder
data visualization
dimension reduction
induction motor fault detection and classification
lattice vector quantization
Summary In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.
ISBN 9781457710926
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048235

Document type: Conference Paper
Collection: Institute for Frontier Materials
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