Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: Editor-in-chief: Santosh Nanda
Open Access
Journal Browser
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


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.

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


Figure of 9


[1]  Watanabe, K., Matsuura, I., Abe, M., Kubota, M. and Himmelblau, D. M., “Incipient fault diagnosis of chemical processes via artificial neural networks,” AIChE Journal, 35 (11), 1803-1812, 1989.
[2]  Koivo, H. N., “Artificial neural networks in fault diagnosis and control,” Control Engineering Practice, 2 (1), 89-101, 1994.
[3]  Sharma, R., Singh, K., Singhal, D. and Ghosh, R., “Neural network applications for detecting process faults in packed towers,” Chemical Engineering and Processing: Process Intensification, 43 (7), 841-847, 2004.
[4]  Behbahani, R. M., Jazayeri-Rad, H. and Hajmirzaee, S, “Fault detection and diagnosis in a sour gas absorption column using neural networks,” Chemical engineering & technology, 32 (5), 840-845, 2009.
[5]  Eslamloueyan, R., “Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process,” Applied Soft Computing, 11 (1), 1407-1415, 2011.
[6]  Vapnik, V., The nature of statistical learning theory. New York, Springer, 1999.
[7]  Widodo, A. and Yang, B.S. “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, 21 (6), 2560-2574, 2007.
[8]  Tyagi, C. S., “A comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing,” Neuron, 1, 309-317, 2008.
[9]  Lee, M. C. and To, C., “Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress,” International Journal of Artificial Intelligence & Applications, 1(3), 31-43, 2010.
[10]  Azar, A. and El-Said, S., “Performance analysis of support vector machines classifiers in breast cancer mammography recognition,” Neural Computing and Applications, 1-15, 2013.
[11]  Chiang, L. H., Kotanchek, M.E. and Kordon, A. K., “Fault diagnosis based on Fisher discriminant analysis and support vector machines,” Computers & chemical engineering, 28 (8), 1389-1401, 2004.
[12]  Yélamos, I., Graells, M., Puigjaner, L. and Escudero, G., “Simultaneous fault diagnosis in chemical plants using a multilabel approach,” AIChE Journal, 53 (11), 2871-2884, 2007.
[13]  Mao, Y., Xia, Z., Yin, Z., Sun, Y. and Wan, Z., “Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature selection,” Chinese Journal of Chemical Engineering, 15(2), 233-239, 2007.
[14]  Yélamos, I., Escudero, G., Graells, M. and Puigjaner, L., “Performance assessment of a novel fault diagnosis system based on support vector machines,” Computers & Chemical Engineering, 33 (1), 244-255, 2009.
[15]  Abe, S., Support vector machines for pattern classification. New York, Springer, 2005.
[16]  Hsu, C. W. and Lin, C. J., “A comparison of methods for multiclass support vector machines,” Neural Networks, IEEE Transactions on, 13(2), 415-425, 2002.
[17]  Whitley, D., “A genetic algorithm tutorial,” Statistics and computing, 4(2), 65-85, 1994.
[18]  Sorsa, T., Koivo, H. N. and Koivisto, H., “Neural networks in process fault diagnosis,” Systems, Man and Cybernetics, IEEE Transactions on, 21 (4), 815-825, 1991.
[19]  Hsu, C. W., Chang, C.C. and Lin, C. J., “A practical guide to support vector classification,” 2010.
[20]  Chang, C.C. and Lin, C. J., “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 2011.