TY - JOUR
T1 - Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning
AU - Seera, Manjeevan
AU - Lim, Chee Peng
AU - Loo, Chu Kiong
N1 - Funding Information:
This research is supported partially by RU014-2013 grant from University of Malaya.
Publisher Copyright:
© 2014, Springer Science+Business Media New York.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/12
Y1 - 2016/12
N2 - In this paper, a hybrid online learning model that combines the fuzzy min–max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks.
AB - In this paper, a hybrid online learning model that combines the fuzzy min–max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks.
KW - Classification and regression tree
KW - Fault detection and diagnosis
KW - Fuzzy min–max neural network
KW - Induction motor
UR - http://www.scopus.com/inward/record.url?scp=84905313499&partnerID=8YFLogxK
U2 - 10.1007/s10845-014-0950-3
DO - 10.1007/s10845-014-0950-3
M3 - Article
AN - SCOPUS:84905313499
SN - 0956-5515
VL - 27
SP - 1273
EP - 1285
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
IS - 6
ER -