TY - JOUR
T1 - Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors
AU - Seera, Manjeevan
AU - Lim, Chee Peng
AU - Ishak, Dahaman
AU - Singh, Harapajan
N1 - Funding Information:
The authors gratefully acknowledge the partial financial support of the FRGS grants (No. 6711229 and 6711195) for this work.
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min-max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.
AB - In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min-max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.
KW - Fault detection and diagnosis
KW - Fuzzy min-max neural network
KW - Induction motor
KW - Motor current signature analysis
KW - Pattern classification
UR - http://www.scopus.com/inward/record.url?scp=84888823881&partnerID=8YFLogxK
U2 - 10.1007/s00521-012-1310-x
DO - 10.1007/s00521-012-1310-x
M3 - Article
AN - SCOPUS:84888823881
SN - 0941-0643
VL - 23
SP - 191
EP - 200
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - SUPPL1
ER -