Fuzzy Filtering of Sensors Signals in Manufacturing Systems with Time Constraints

  • Anis M’halla Ecole Nationale d’Ingénieurs de Tunis Unité de recherche LARA-Automatique BP 37, Le Belvédère, 1002 Tunis, Tunisie
  • Nabil Jerbi Ecole Nationale d’Ingénieurs de Tunis Unité de recherche LARA-Automatique BP 37, Le Belvédère, 1002 Tunis, Tunisie
  • Simon Collart Dutilleul Laboratoire d’Automatique, Génie Informatique et Signal Cité Scientifique, BP 48, 59651 Villeneuve d’Ascq, France
  • Etienne Craye Laboratoire d’Automatique, Génie Informatique et Signal Cité Scientifique, BP 48, 59651 Villeneuve d’Ascq, France
  • Mohamed Benrejeb Ecole Nationale d’Ingénieurs de Tunis Unité de recherche LARA-Automatique BP 37, Le Belvédère, 1002 Tunis, Tunisie

Abstract

The presented work is dedicated to the supervision of manufacturing job-shops with time constraints. Such systems have a robustness property towards time disturbances. The main contribution of this paper is a fuzzy filtering approach of sensors signals integrating the robustness values. This new approach integrates a classic filtering mechanism of sensors signals and fuzzy logic techniques. The strengths of these both techniques are taken advantage of the avoidance of control freezing and the capability of fuzzy systems to deal with imprecise information by using fuzzy rules. Finally, to demonstrate the effectiveness and accuracy of this new approach, an example is depicted. The results show that the fuzzy approach allows keeping on producing, but in a degraded mode, while providing the guarantees of quality and safety based on expert knowledge integration.

References

[1] A. Boufaied, A. Subias, and M. Combacau, Distributed Fault Detection with Delays Consideration, 15th International Workshop on Principles of Diagnosis, Carcassonne, June 2004.

[2] A. Boufaied, A. Subias, and M. Combacau, The Distributed time constraints verification modelled with time Petri nets, 17th IMACS Word Congress on Scientific Computation, Applied Mathematics and Simulation (IMACS'05), Paris, July 2005.

[3] N. Jerbi, S. Collart Dutilleul, E. Craye, and M. Benrejeb, Time Disturbances and Filtering of Sensors Signals in Tolerant Multi-product Job-shops with Time Constraints, International Journal of Computers, Communications & Control, Vol. 1, No. 4, pp. 61 – 72, 2006.
http://dx.doi.org/10.15837/ijccc.2006.4.2308

[4] A. Toguyeni, Surveillance et diagnostic en ligne dans les ateliers flexibles de l'industrie manufacturière, Ph.D. Thesis, Université des Sciences et Technologies de Lille, November 1992.

[5] N. Jerbi, S. Collart Dutilleul, E. Craye, and M. Benrejeb, Robust Control of Multiproduct Job-shops in Repetitive Functioning Mode, IEEE Conference on Systems, Man, and Cybernetics (SMC'04), The Hague, Vol. 5, pp. 4917 – 4922, October 2004.

[6] N. Sawaya, and B. Ghaddar, A Fuzzy Logic Approach for Adjusting the Contention Window Size in IEEE 802.11e Wireless Ad hoc Networks, IEEE International Symposium on Communication, Control, and Signal Processing (IEEE ISCCSP), Marrakech 2006.

[7] J. Bas, A. Pérez, and M. Lagunas, Differential fuzzy filtering for adaptive line enhancement in spread spectrum communications, Signal Processing Journal, Vol. 86, Issue 5, pp 984 – 1009, May 2006.
http://dx.doi.org/10.1016/j.sigpro.2005.08.002

[8] D. Van De Ville, M. Nachtegael, D. Van der Weken, E. Kerre, and W. Philips, Noise Reduction by Fuzzy Image Filtering, IEEE Transactions on Fuzzy System, Vol. 11, No. 4, pp. 429 – 436, August 2003.
http://dx.doi.org/10.1109/TFUZZ.2003.814830

[9] R. Mikut, A. Lehmann, and G. Bretthauer, Fuzzy Stability Supervision of Robot Grippers, IEEE International Conference on Fuzzy Systems, Budapest, Vol. 3, pp. 1473 – 1478, July 2004.
http://dx.doi.org/10.1109/fuzzy.2004.1375391

[10] L. Zadeh, Knowledge Representation in Fuzzy Logic, IEEE Transactions on Knowledge And Data Engineering, Vol. 1, No. 1, pp. 89 – 100, March 1989.
http://dx.doi.org/10.1109/69.43406

[11] C. Teng Lin, An Adaptive Neural Fuzzy Filter and Its Applications, IEEE Transactions on Systems Man and Cybernetics, Vol. 27, No. 4, pp. 635 – 656, August 1997.
http://dx.doi.org/10.1109/3477.604107

[12] F. Lotte, A. L’ecuyer, F. Lamarche, and B. Arnaldi, Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 15, No. 2, June 2007.

[13] Z. Shafiq, F. Muddassar, and S. Khayam, A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection (EvoWorkshop), pp. 52 – 61, 2008.
http://dx.doi.org/10.1007/978-3-540-78761-7_6

[14] J.L. Castro, J.M. Benitez, and I. Requena, Are artificial neural networks black boxes?, IEEE Transaction on Neural Networks, Vol. 8, No. 5, pp.1156 – 1164, September 1997.
http://dx.doi.org/10.1109/72.623216
Published
2010-09-01
How to Cite
M’HALLA, Anis et al. Fuzzy Filtering of Sensors Signals in Manufacturing Systems with Time Constraints. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 5, n. 3, p. 362-374, sep. 2010. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2488>. Date accessed: 09 aug. 2020. doi: https://doi.org/10.15837/ijccc.2010.3.2488.

Keywords

Alarm filtering, fuzzy logic, symptoms generation, robustness, time constraints, manufacturing