A hybrid FMM-CART model for fault detection and diagnosis of induction motors

Manjeevan Seera, Chee Peng Lim, Dahaman Ishak

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


A new approach to detect and classify fault conditions of induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and the 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 experiments using real data measurements of motor currents from healthy and faulty induction motors is conducted. FMM-CART is able to detect and classify the associated inductor motor faults with good accuracy rates. Useful 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
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Number of pages7
EditionPART 3
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference on Neural Information Processing 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011
Conference number: 18th
https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7064 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2011
Abbreviated titleICONIP 2011
Internet address


  • classification and regression tree
  • Fault detection and diagnosis
  • fuzzy min-max neural network
  • induction motor

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