Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

V.T. Tran, Bo-Suk Yang, Myung-Suck Oh, Andy Chit Chiow Tan

Research output: Contribution to journalArticleAcademicpeer-review

153 Citations (Scopus)

Abstract

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.
Original languageEnglish
Pages (from-to)1840-1849
JournalExpert systems with applications
Volume36
Issue number2
DOIs
Publication statusPublished - 2009
Externally publishedYes

Keywords

  • Fault diagnosis
  • Induction motors
  • Adaptive neuro-fuzzy inference
  • Decision trees

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