Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: http://www.sciepub.com/journal/automation Editor-in-chief: Santosh Nanda
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Journal of Automation and Control. 2014, 2(1), 1-7
DOI: 10.12691/automation-2-1-1
Open AccessArticle

Process Fault Diagnosis Using Support Vector Machines with a Genetic Algorithm based Parameter Tuning

Mehdi Namdari1, , Hooshang Jazayeri-Rad1 and Seyed-Jalaladdin Hashemi1

1Department of Instrumentation and Automation, Petroleum University of Technology, Ahwaz, Iran

Pub. Date: December 24, 2013

Cite this paper:
Mehdi Namdari, Hooshang Jazayeri-Rad and Seyed-Jalaladdin Hashemi. Process Fault Diagnosis Using Support Vector Machines with a Genetic Algorithm based Parameter Tuning. Journal of Automation and Control. 2014; 2(1):1-7. doi: 10.12691/automation-2-1-1

Abstract

Fault diagnosis, centered on pattern recognition techniques employing online measurements of process data, has been studied during the past decades. Amongst those techniques, artificial neural networks classifiers received an enormous attention due to some of their remarkable features. Recently, a new machine learning method based on statistical learning theory known as the Support Vector Machine (SVM) classifier is offered in the pattern recognition field. Support vector machine classifiers were originally used to solve binary classification problems. Subsequently, methods were proposed to apply support vector machine classifier to multiclass problems. Two of these mostly used methods are known as one versus one and one versus all. This paper deals with the application of the above mentioned classifiers for fault diagnosis of a chemical process containing a continuous stirred tank reactor and a heat exchanger. The results show a superior classification performance of the support vector machine versus the selected artificial neural network. In addition, the support vector machine classifier is very sensitive to the proper selection of the training parameters. It is shown that the utilization of genetic algorithm for optimal selection of these parameters is feasible and can help to improve the support vector machine classifier performance.

Keywords:
process fault diagnosis continuous stirred tank reactor support vector machine artificial neural network SVM Parameter Tuninge

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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