DocumentCode :
1399060
Title :
Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model
Author :
Seera, M. ; Chee Peng Lim ; Ishak, D. ; Singh, H.
Author_Institution :
Sch. of Electr. & Electron. Eng., Univ. of Sci. Malaysia, Nibong Tebal, Malaysia
Volume :
23
Issue :
1
fYear :
2012
Firstpage :
97
Lastpage :
108
Abstract :
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.
Keywords :
decision trees; electric machine analysis computing; fault diagnosis; fuzzy set theory; induction motors; minimax techniques; neural nets; pattern classification; regression analysis; rotors; stators; broken rotor bars; classification and regression tree; data classification; decision tree; discriminative input feature extraction; eccentricity problems; fault detection; fault diagnosis; hybrid FMM-CART model; hybrid fuzzy min-max neural network; induction motors; motor current signature analysis; rule extraction problems; signal harmonics; spectral density; stator current signatures; stator winding faults; unbalanced voltages; Bars; Fault detection; Feature extraction; Induction motors; Rotors; Stator windings; Classification and regression tree; fault detection and diagnosis; fuzzy min–max neural network; induction motor; motor current signature analysis;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2011.2178443
Filename :
6104222
Link To Document :
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