TY - JOUR
T1 - Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model
AU - Seera, Manjeevan
AU - Lim, Chee Peng
AU - Ishak, Dahaman
AU - Singh, Harapajan
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.
AB - In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.
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 - http://www.scopus.com/inward/record.url?scp=84885095960&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2013.08.002
DO - 10.1016/j.asoc.2013.08.002
M3 - Article
AN - SCOPUS:84885095960
SN - 1568-4946
VL - 13
SP - 4493
EP - 4507
JO - Applied Soft Computing
JF - Applied Soft Computing
IS - 12
ER -