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

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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.

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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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Acknowledgments

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

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Manjeevan Seera & Chu Kiong Loo

  2. Centre for Intelligent Systems Research, Deakin University, Geelong, Victoria, Australia

    Chee Peng Lim

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  1. Manjeevan Seera
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  2. Chee Peng Lim
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Correspondence to Manjeevan Seera.

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Seera, M., Lim, C.P. & Loo, C.K. Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning. J Intell Manuf 27, 1273–1285 (2016). https://doi.org/10.1007/s10845-014-0950-3

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