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

Tran, Van Tung, Yang, Bo-Suk, Oh, Myung-Suck and Tan, Andy Chit Chiow (2009) Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems With Applications, 36 (2). pp. 1840-1849. ISSN 0957-4174

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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.

Item Type: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering
Depositing User: Van Tran
Date Deposited: 31 Jan 2013 12:37
Last Modified: 03 Dec 2016 23:46
URI: http://eprints.hud.ac.uk/id/eprint/16575

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