TY - JOUR
T1 - Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART Model
AU - Seera, Manjeevan
AU - Lim, Chee Peng
AU - Ishak, Dahaman
AU - Singh, Harapajan
N1 - Funding Information:
Manuscript received January 14, 2011; revised October 15, 2011; accepted October 16, 2011. Date of publication December 15, 2011; date of current version January 5, 2012. This work was supported in part by the Research University Grant of University of Science Malaysia under Grant 814089.
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Classification and regression tree
KW - fault detection and diagnosis
KW - fuzzy min-max neural network
KW - induction motor
KW - motor current signature analysis
UR - https://www.scopus.com/pages/publications/84872036650
U2 - 10.1109/TNNLS.2011.2178443
DO - 10.1109/TNNLS.2011.2178443
M3 - Article
AN - SCOPUS:84872036650
SN - 2162-237X
VL - 23
SP - 97
EP - 108
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 1
ER -