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Automatic Fault Identification of a Mechanical System using Genetic Algorithm

by Abhishek Bal, Nilima Paul, Sonali Sen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 9
Year of Publication: 2014
Authors: Abhishek Bal, Nilima Paul, Sonali Sen
10.5120/18232-9201

Abhishek Bal, Nilima Paul, Sonali Sen . Automatic Fault Identification of a Mechanical System using Genetic Algorithm. International Journal of Computer Applications. 104, 9 ( October 2014), 25-31. DOI=10.5120/18232-9201

@article{ 10.5120/18232-9201,
author = { Abhishek Bal, Nilima Paul, Sonali Sen },
title = { Automatic Fault Identification of a Mechanical System using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 9 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number9/18232-9201/ },
doi = { 10.5120/18232-9201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:35:44.008066+05:30
%A Abhishek Bal
%A Nilima Paul
%A Sonali Sen
%T Automatic Fault Identification of a Mechanical System using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 9
%P 25-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes a fault identification technique for mechanical system which is based on genetic algorithm using training set. The real-world application of Genetic Algorithm (GA) to the key of engineering problem becomes a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are convenient for searching a space in multi-directional way from large spaces and poorly defined space. In this paper Genetic Algorithm is used to identify and evaluate the fault cases. Several methods are employed in the state of art in fault identification. Here one class of efficient method are investigated which is based on optimization technique. Here it is shown that Genetic Algorithm can be used to select smaller subset of features from the large set which together form a new set that can be successful for fault identification and classification tasks. The performance of this present proposed method has been verified through two types of fitness function, namely, square function and polynomial function. Finally, fault detection exercises are performed based on the training set to verify the feasibility of this proposed method. Experimental results show that the fault is distinguished with a high precision through this present work.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Fault identification Optimization Fitness Function Training set.

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