Identification and classification protocol for complex systems

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Published Jul 8, 2014
N-H. Tran M-F. Bouaziz E. Zamaï

Abstract

This paper proposes a test protocol for drift identification and classification in a complex production system. The key objective here is to develop a classifier for failure causes where variables depend on a set of measured parameters. In the context of our work, we assume that the drift problem of a production system is generally observed in control products phase. The model proposed in this paper for failure causes classification is structured in the form of a causeseffects graph based on Hierarchical Naïve Bayes formalism (HNB). Our key contribution in this is the methodology that
allows developing failure causes classification test model in the complex and uncertain manufacturing context.

How to Cite

Tran, N.-H., Bouaziz, M.-F., & Zamaï, E. (2014). Identification and classification protocol for complex systems. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1518
Abstract 356 | PDF Downloads 102

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Keywords

classification, maintenance, decision support, Equipment health factor, Hierarchical Naïve Bayes networks, Complex systems

References
Bayes T., (1763). An Essay towards solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London, vol. 53, pages 370-418.
Belur V. D, (1991) Nearest Neighbor: Pattern Classification Techniques , IEEE Computer Society.
Bouaziz. M.-F, Zamaï. E, Duvivier. F. (2013). Towards Bayesian Network Methodology for Predicting the equipment Health Factor of Complex Semiconductor Systems. International Journal of Production Research, Volume 51, Issue 15, 4597-4617.
Bouillaut L., Leray P., Aknin P., François O., Dubois S., (2008). Dynamic Bayesian Networks Modelling Maintenance Strategies: Prevention of Broken Rails. WCRR'08 World Congress on Railway Research, Séoul. Corea.
Bishop, C. M. and Tipping M. E., (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 281–293.
Cover.T.M and Hart. P.E, (1967). Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13 :21–27.
Heckerman D., (1998). A Tutorial on Learning with Bayesian Network. JORDAN M. I., Ed., Learning in Graphical Models, Kluwer Academic Publishers, Boston.
Li H. and Xiao D-Y. , (2011). Faut diagnosic of Tennessee Eastman process using signal geometry matching technique . EURASIP Journal on Advance in Signal Processing 2011:83.
Jensen F.V., (1996). Introduction to Bayesian networks, UCL Press, London.
Kappen H., (2002). The cluster variation method for approximate reasoning in medical diagnosis. Eds., Modeling Bio-medical signals,World-Scientic.
Kunio S., Mitsugu K., Yoshifumi K., (1995). An Advanced step in TPM Implementation. (pages 64-65). Paris, France.
Lyman. PR, Georgakis. C. (1995). Plan-wide control of the Tennesse Eastman problem. Comput Chem Eng, 19(3), 321-331. USA.
Murphy K., (2001). The BayesNet Toolbox for Matlab. Computing Science and Statistics: Proceedings of Infence, vol. 33.
Naim P., Wuillemin P.H., Leray P., Pourret O., (2007). Bayesian Network. 3e édition, Eyrolles (eds).
Neal R.M., Hinton G.E., (1998). A View of the EM algorithm that justifies incre-mental, sparse and other variants. JORDAN M. I., Ed., Learning in Graphical Models, Kluwer Academic Publishers, Boston.
Pearl J., (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan – Kaufmann, San Diego.
Verron. S., Li. J., Tiplica, T. (2010). Fault detection and isolation of faults in a multivariate process with Bayesian network. Journal of Process Control 20 (8), 902-911.
Zio, E.. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety journal. Volume 94, pp. 125-141.
Zaarour I., Heutte L., Leray P., Labiche J., Eter B., Mellier D., (2004). Clustering And Bayesian Network Approaches For Discovering Handwriting Strategies Of Primary School Children. IJPRAI 18(7):1233-1251.
Section
Technical Papers

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