FDI based on WD combined With PEM and RBF networks: Application to the diagnosis of TECP reactor

18th Mediterranean Conference on Control and Automation, MED'10 - Conference Proceedings

Volumn , Issue , 2010, Pages 1347-1352

  Barakat, M ^a,b   Lefebvre, D ^a   Kalil, M ^b   Mustapha, O ^c   Druaux, F ^a  

^a UNIVERSITÉ DU HAVRE   (France)

^b LEBANESE UNIVERSITY   (Lebanon)

^c ISLAMIC UNIVERSITY OF LEBANON   (Lebanon)

Author keywords

[No Author keywords available]

Indexed keywords

DECOMPOSITION TECHNIQUE; ELIMINATION METHOD; FAULT DETECTION AND ISOLATION; HIGH FREQUENCY; INPUT SIGNAL; LOW FREQUENCY; PARAMETERS SELECTION; RBF NETWORK; STATISTICAL PARAMETERS; TENNESSEE EASTMAN CHALLENGE;

CHEMICAL REACTORS; FAULT DETECTION; LARGE SCALE SYSTEMS; SIGNAL DETECTION; SIGNAL PROCESSING; WAVELET DECOMPOSITION;

RADIAL BASIS FUNCTION NETWORKS;

EID: 77956993160     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MED.2010.5547861     Document Type: Conference Paper

References (15)

1. Asokan. A and Sivakumar, "Model based fault detection and diagnosis using structured residual approach in a multi-input, multioutput system," Serbian journal of electrical engineering, vol. 4, nb. 2, November 2007.
2. Mustapha O, Khalil M, Hoblos G, Chafouk H and Lefebvre D, "Fault detection algorithm based on filters bank derived from wavelet packets", in "Robotics, Automation and Control", I -Tech, pp. 183 - 204, 2008.
3. Mustapha O, Khalil M, Hoblos G, Chafouk H and Lefebvre D, "Fault detection algorithm using DCS method combined with filters bank derived from the wavelet transform", International journal of innovative computing, ISSN 1349-4198, vol. 5, nb. 4, April 2009.
4. Ohba K and Ikeuchi K, "Detectability, uniqueness, and reliability of eigen windows for stable verification of partially occluded objects", IEEE Transactions on Pattern Analysis and Machine Intelligenve, vol. 19, nb. 9, pp. 1043-1048, 1997.
5. Murase H and Nayar S, "Visual learning and recognition of 3D objects from appearance", Interantional Journal of Computer Vision, vol. 14, pp. 5-24, 1995.
6. Merry R and Steinbuch M, "Wavelet theory and applications", literature study, Eindhoven university of technology, Department of mechanical engineering, Control systems technology group, June7, 2005.
7. Arthur A, "Signal processing applications of wavelets", University of california, Information and computer sciences, USA.
8. Young L and Sung K, "Properties of random signals in wavelet domain" vol. 3, pp. 107-114, South Korea, 1999.
9. Venkatesan P and Anitha S, "Application of a radial basis function neural network for diagnosis of diabetes mellitus", Tuberculosis Research Centre, India, 2006.
10. Hastie T. and Stuetzle W., Principal curves. Journal of the American Statistical Association, vol. 84, pages 502-516, 1989.
11. Kramer M. A., Nonlinear principal component analysis using auto-associative neural networks, AIChE Journal, vol. 37, pages 233-243, 1991.
12. Webb A. R., An approach to nonlinear principal component analysis using radially symmetric kernel functions. Statist. Comput., vol. 6, 1996.
13. Downs, J.J., Vogel, E.F, A plant-wide industrial control problem, Computers and Chemical Engineering, Vol 17, pp. 245-255. 1993.
14. Leclercq, E., Druaux, F. Lefebvre, D., Zerkaoui, S., Autonomous learning algorithm for fully connected recurrent networks. Neurocomputing, vol. 63, pp. 25-44, 2005.
15. Zerkaoui S., Druaux F., Leclercq E., Lefebvre D., Multivariable adaptive control for non-linear systems: application to the Tennessee Eastman Challenge Process, ECC 2007, Kos, Greece, 2007.