Online motor fault detection and diagnosis using a hybrid FMM-CART model

Seera, Manjeevan and Lim, Chee Peng 2014, Online motor fault detection and diagnosis using a hybrid FMM-CART model, IEEE transactions on neural networks and learning systems, vol. 25, no. 4, pp. 806-812.

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Title Online motor fault detection and diagnosis using a hybrid FMM-CART model
Author(s) Seera, Manjeevan
Lim, Chee Peng
Journal name IEEE transactions on neural networks and learning systems
Volume number 25
Issue number 4
Start page 806
End page 812
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2014-03-10
ISSN 2162-237X
2162-2388
Keyword(s) classification and regression tree (CART)
electrical motors
fuzzy min-max (FMM) neural network
online fault detection and diagnosis (FDD)
rule extraction
Summary In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061685

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