Novel Agent Based-approach for Industrial Diagnosis: A Combined use Between Case-based Reasoning and Similarity Measure
DOI:
https://doi.org/10.9781/ijimai.2016.4211Keywords:
Collaboration, Case-Based Reasoning, Agents, DiagnosisAbstract
In spunlace nonwovens industry, the maintenance task is very complex, it requires experts and operators collaboration. In this paper, we propose a new approach integrating an agent- based modelling with case-based reasoning that utilizes similarity measures and preferences module. The main purpose of our study is to compare and evaluate the most suitable similarity measure for our case. Furthermore, operators that are usually geographically dispersed, have to collaborate and negotiate to achieve mutual agreements, especially when their proposals (diagnosis) lead to a conflicting situation. The experimentation shows that the suggested agent-based approach is very interesting and efficient for operators and experts who collaborate in INOTIS enterprise.Downloads
References
[1] K. Allem, R. Maamri, Z Sahnoun. «Un Modèle SMA pour le Diagnostic Collectif.»
[2] INOTIS. (2012, January 9).INOTIS enterprise [Online]. Available: http://www.inotis.com
[3] F.Z. Benkaddour, N. Taghezout, B. Ascar, “Towards a Novel Approach for Enterprise Knowledge Capitalization Utilizing an
Ontology and Collaborative Decision-Making: Application to Inotis Enterprise.”International Journal of Decision Support System Technology (IJDSST) 8.1 (2016): 1-24.
[4] Kolodner, L. Janet, “An introduction to case-based reasoning.” Artificial Intelligence Review 6.1 (1992): 3-34.
[5] Pal, K. Sankar, and C.K Simon, Shiu, Foundations of soft case-based reasoning. Vol. 8. John Wiley & Sons, 2004.
[6] S. H. Ji, M. Park, H. S. Lee, Y. S. Yoon, “Similarity measurement method of case-based reasoning for conceptual cost estimation.” Proceedings of International Conference on Computing in Civil and Building Engineering. 2010.
[7] I. Rasovska, Contribution à une méthodologie de capitalisation des connaissances basée sur le raisonnement à partir de cas: Application au diagnostic dans une plateforme d’e-maintenance. Diss. Université de Franche-Comté, 2006.
[8] C. El Aoun, H. B. Ayed, H. Eleuch, E. Aïmeur, F. Kamoun, «Le Raisonnement à Base de Cas Appliqué à la Négociation Electronique.» 5th International Conference: Sciences of Electronic, Technologies of Information and Télécommunications (SETIT 2009). 2009.
[9] J. Cai, Y. Jia, C. Gu, W. Wu,”Research of wartime equipment maintenance intelligent decision-making based on case-based reasoning.” Procedia Engineering 15 (2011): 163-167.
[10] P. P. Ruiz, D. Noyes, B. Kamsu-Foguem, «Raisonnement collaboratif à partir de cas dans la résolution de problèmes en maintenance.»9th International Conference on Modeling, Optimization & Simulation. 2012.
[11] R. H. C. Palácios, I. N. da Silva, A. Goedtel, W. F. Godoy, “A novel multiagent approach to identify faults in line connected three-phase induction motors.” Applied Soft Computing45 (2016): 1-10.
[12] M. J. Mendes, J. M. Calado, J. S. da Costa, “Fault diagnosis system based in agents.” Fault Detection, Supervision and Safety of Technical Processes. Vol. 6. No. 1. 2006.
[13] J. Pitt, L. Kamara, M. Sergot, A. Artikis, “Voting in multi-agent systems.” The Computer Journal 49.2 (2006): 156-170.
[14] M. A. Mahmoud, M. S. Ahmad, M. Z. M. Yuso, A. Idrus, «An Automated Negotiation-based Framework via Multi-Agent System for the Construction Domain.” International Journal of Artificial Intelligence and Interactive Multimedia 3.5 (2015): 23-27.
[15] H. B. Baron, M. M. Rojas, R. G. Crespo, O. S. Martinez, “A multiagent matchmaker based on hidden markov model for decentralized grid scheduling.” Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on. IEEE, 2012.
[16] E. Torres, O. Sanjuan, L. Joyanes, J. D. García, R. González, “An Architecture For Management Of Distributed And Redundant Web Storage With Ingelligent Agent Systems And Emerging Techniques.” IEEE Latin America Transactions 6.6 (2008): 524-528.
[17] R. Rada, H. Mili, E. Bicknell, M. Blettner, “Development and application of a metric on semantic nets.”Systems, Man and Cybernetics, IEEE Transactions on 19.1 (1989): 17-30.
[18] Z. Wu, M. Palmer, “Verbs semantics and lexical selection.”Proceedings of the 32nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 1994.
[19] N. Seco, T. Veale, J. Hayes. “An intrinsic information content metric for semantic similarity in WordNet.” ECAI. Vol. 16. 2004.
[20] B. Köppen-Seliger, T. Marcu, M. Capobianco, S. Gentil, M. Albert, S. Latzel, “MAGIC: An integrated approach for diagnostic data management and operator support.” Fault Detection, Supervision and Safety of Technical Processes 2003 (SAFEPROCESS 2003): A Proceedings Volume from the 5th IFAC Symposium, Washington, DC, USA, 9-11 June 2003. Vol. 1. Elsevier, 2004.
[21] M. Albert, T. Längle, H. Woern. Development tool for distributed monitoring and diagnosis systems. KARLSRUHE UNIV (GERMANY FR), 2002.
[22] B. L. Iantovics, “Cooperative Medical Diagnosis Elaboration by Physicians and Artificial Agents.” From System Complexity to Emergent Properties. Springer Berlin Heidelberg, 2009. 315-339.
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