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

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...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications 2011-08, Vol.38 (8), p.10205-10209
Main Authors: Hajnayeb, A., Ghasemloonia, A., Khadem, S.E., Moradi, M.H.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural network
Classification
Diagnosis
Error detection
Expert systems
Fault detection
Faults
Feature selection
Gearboxes
Genetic Algorithm
Genetic algorithms
Vibration analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.02.065