Journal of Intelligent Manufacturing

, Volume 27, Issue 6, pp 1273–1285 | Cite as

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

Article
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Abstract

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.

Keywords

Classification and regression tree Fault detection and diagnosis Fuzzy min–max neural network Induction motor 

Abbreviations

ARTMAP

Adaptive resonance theory mapping

CART

Classification and regression trees

CWRU

Case Western Reserve University

FFT

Fast Fourier transform

FMM

Fuzzy min–max

ID3

Iterative dichotomiser 3

LM

Levenberg–Marquardt

MCSA

Motor current signature analysis

MLP

Multi-layered perceptron

NEMA

National Electrical Manufacturers Association

PSD

Power spectral density

RSH

Rotor slot harmonics

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Notes

Acknowledgments

This research is supported partially by RU014-2013 grant from University of Malaya.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Manjeevan Seera
    • 1
  • Chee Peng Lim
    • 2
  • Chu Kiong Loo
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityGeelongAustralia

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