Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis

被引：123

作者：

Hajnayeb, A. ^{[1
]}

Ghasemloonia, A. ^{[2
]}

Khadem, S. E. ^{[1
]}

Moradi, M. H. ^{[3
]}

机构：

[1] Tarbiat Modares Univ, Dept Mech Engn, Tehran, Iran

[2] Mem Univ Newfoundland, Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada

[3] Amirkabir Univ, Dept Biomed Engn, Tehran, Iran

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 08期

关键词：

Artificial neural network; Diagnosis; Vibration analysis; Genetic Algorithm; Feature selection; ARTIFICIAL NEURAL-NETWORK; VIBRATION;

D O I：

10.1016/j.eswa.2011.02.065

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a system based on artificial neural networks (ANNs) was designed to diagnose different types of fault in a gearbox. An experimental set of data was used to verify the effectiveness and accuracy of the proposed method. The system was optimized by eliminating unimportant features using a feature selection method (UTA method). Consequently, the fault detection system operates faster while the classification error decreases or remains constant in some other cases. This method of feature selection is compared with Genetic Algorithm (GA) results. The findings verify that the results of the UTA method are as accurate as GA, despite its simple algorithm. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：10205 / 10209

页数：5

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