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

Manjeevan Seera, Chee Peng Lim, Chu Kiong Loo

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1273-1285
Number of pages13
JournalJournal of Intelligent Manufacturing
Volume27
Issue number6
DOIs
Publication statusPublished - Dec 2016
Externally publishedYes

Keywords

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

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