Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils

Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regre...

Full description

Saved in:
Bibliographic Details
Published in:Engineering computations 2016-06, Vol.33 (4), p.995-1005
Main Authors: Fernandez-Lozano, Carlos, Cedrón, Francisco, Rivero, Daniel, Dorado, Julian, Andrade-Garda, José Manuel, Pazos, Alejandro, Gestal, Marcos
Format: Article
Language:English
Subjects:
Aerospace engineering
Atomic spectra
Chemical properties
Copper
Datasets
Design analysis
Engineering
Fuzzy
Genetic algorithms
Laboratories
Lubricants
Lubricants & lubrication
Mathematical models
Methods
Mutation
Neural networks
Organic chemistry
Principal components analysis
Quality assessment
Radiation
Regression
Regression analysis
Regression models
Scientific imaging
Support vector machines
Tournaments & championships
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:Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.
ISSN:0264-4401
1758-7077
DOI:10.1108/EC-03-2015-0062