Share This Article:

Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine

Abstract Full-Text HTML Download Download as PDF (Size:702KB) PP. 513-516
DOI: 10.4236/epe.2011.34062    5,562 Downloads   8,845 Views   Citations

ABSTRACT

The real-time fault diagnosis system is very important for steam turbine generator set due serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diagnosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes district. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the designed system will be better positioned for irregularities detection during the production process.

Cite this paper

D. Benazzouz, S. Benammar and S. Adjerid, "Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine," Energy and Power Engineering, Vol. 3 No. 4, 2011, pp. 513-516. doi: 10.4236/epe.2011.34062.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] M. Basseville and M.-O. Cordier, “Surveillance et Diag- Nostic de Systèmes Dynamiques: Approche Complémentaire du Traitement de Signal et de l’Intelligence Artifi- Cielle,” Rapport INRIA, Thèmes 3-4, No. 2861, 1996, pp. 1-40.
[2] N. Palluat, D. Racoceanu and N. Zerhouni, “Utilisation des Réseaux de Neurones Temporels pour le Pronostic et la Surveillance Dynamique,” Etude Comparative de Trois Réseaux de Neurones Récurrents, RSTI-RIA, Vol. 19, No. 6, 2005, pp. 911-948.
[3] M. Bouamar and M. Ladjal, “Système Multicapteur Utilisant les Réseaux de Neurones Artificiels pour la Surveillance des Eaux Potables,” 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, LASS, Laboratoire d’Analyse des Signaux et Systèmes, Université de M’sila, Algérie, 25-29 March 2007.
[4] M. I. A. Lourakis, “A Brief Description of the Levenberg-Marquardt Algorithm,” Implemented Institute of Computer Science Foundation for Research and Technology—Hellas (FORTH) Vassilika Vouton, Heraklion, 2005.
[5] J. Karim, “Surveillance, Diagnostic et Pronostic en Temps Réel de Systèmes Hybrides: Application à des Bancs d’Essais CERTIA,” LAAS-CNRS Groupe DISCO 7, Toulouse, 2007.
[6] T. Alani, “Réseaux de Neurones Tutorial en Matlab,” Département Informatique ESIEE-Paris, Paris, 2008.
[7] M. S. Patil, Jose Mathew and P. K. R. Kumar, “Bearing Signature Analysis as a Medium For Fault Detection: A Review,” Journal of Tribology, Vol. 130, No. 1, 2008, pp. 1-7. doi:10.1115/1.2805445

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.