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, doi: 10.1109/TNNLS.2013.2280280.

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Title Online motor fault detection and diagnosis using a hybrid FMM-CART model
Author(s) Seera, Manjeevan
Lim, Chee PengORCID iD for 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
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
DOI 10.1109/TNNLS.2013.2280280
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
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