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Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

Seera, Manjeevan, Lim, Chee Ping and Loo, Chu Kiong 2016, Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning, Journal of intelligent manufacturing, vol. 27, no. 6, pp. 1273-1285, doi: 10.1007/s10845-014-0950-3.

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Title Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning
Author(s) Seera, Manjeevan
Lim, Chee PingORCID iD for Lim, Chee Ping orcid.org/0000-0003-4191-9083
Loo, Chu Kiong
Journal name Journal of intelligent manufacturing
Volume number 27
Issue number 6
Start page 1273
End page 1285
Total pages 13
Publisher Springer
Place of publication New York, N.Y.
Publication date 2016-12
ISSN 0956-5515
1572-8145
Keyword(s) classification and regression tree
fault detection and diagnosis
fuzzy min-max neural network
induction motor
Summary 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. © 2014 Springer Science+Business Media New York.
Language eng
DOI 10.1007/s10845-014-0950-3
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071225

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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