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

被引：154

作者：

Tran, Van Tung ^{[1
]}

Yang, Bo-Suk ^{[1
]}

Oh, Myung-Suck ^{[1
]}

Tan, Andy Chit Chiow ^{[2
]}

机构：

[1] Pukyong Natl Univ, Sch Mech Engn, Pusan 608739, South Korea

[2] Queensland Univ Technol, Sch Mech Mfg & Med Engn, Brisbane, Qld 4001, Australia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

关键词：

Fault diagnosis; Induction motors; Adaptive neuro-fuzzy inferences; Decision trees; FEATURE-SELECTION; SYSTEM; COMBINATION; ANFIS;

D O I：

10.1016/j.eswa.2007.12.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a fault diagnosis method based oil 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 arc used. The results indicate that the CART-ANFIS model has potential for fault diagnosis of induction motors. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1840 / 1849

页数：10

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