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

Seera, M. and Lim, C.P. and Loo, C.K. (2016) Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning. Journal of Intelligent Manufacturing, 27 (6). pp. 1273-1285. ISSN 0956-5515, DOI

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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.

Item Type: Article
Funders: This research is supported partially by RU014-2013 grant from University of Malaya
Uncontrolled Keywords: Classification and regression tree; Fault detection and diagnosis; Fuzzy min–max neural network; Induction motor
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Sep 2017 04:55
Last Modified: 25 Feb 2019 02:29

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