Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART Model

Manjeevan Seera, Chee Peng Lim, Dahaman Ishak, Harapajan Singh

Research output: Contribution to journalArticleResearchpeer-review

141 Citations (Scopus)


In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Original languageEnglish
Pages (from-to)97-108
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Publication statusPublished - 2012
Externally publishedYes


  • Classification and regression tree
  • fault detection and diagnosis
  • fuzzy min-max neural network
  • induction motor
  • motor current signature analysis

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