Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

Seera, Manjeevan, Lim, Chee Peng, Ishak, Dahaman and Singh, Harapajan 2012, Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model, IEEE transactions on neural networks and learning systems, vol. 23, no. 1, pp. 97-108.

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Title Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model
Author(s) Seera, Manjeevan
Lim, Chee Peng
Ishak, Dahaman
Singh, Harapajan
Journal name IEEE transactions on neural networks and learning systems
Volume number 23
Issue number 1
Start page 97
End page 108
Total pages 12
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2012-01-02
ISSN 2162-237X
2162-2388
Keyword(s) classification and regression tree
fault detection and diagnosis
fuzzy min–max neural network
induction motor
motor current signature analysis
Summary In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048769

Document type: Journal Article
Collection: Institute for Frontier Materials
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