Commenced in January 2007
Paper Count: 31464
Bond Graph and Bayesian Networks for Reliable Diagnosis
Abstract:Bond Graph as a unified multidisciplinary tool is widely used not only for dynamic modelling but also for Fault Detection and Isolation because of its structural and causal proprieties. A binary Fault Signature Matrix is systematically generated but to make the final binary decision is not always feasible because of the problems revealed by such method. The purpose of this paper is introducing a methodology for the improvement of the classical binary method of decision-making, so that the unknown and identical failure signatures can be treated to improve the robustness. This approach consists of associating the evaluated residuals and the components reliability data to build a Hybrid Bayesian Network. This network is used in two distinct inference procedures: one for the continuous part and the other for the discrete part. The continuous nodes of the network are the prior probabilities of the components failures, which are used by the inference procedure on the discrete part to compute the posterior probabilities of the failures. The developed methodology is applied to a real steam generator pilot process.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071886Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2187
 R. J. Patton and J. Chen, "Observer-based fault detection and isolation : Robustness and applications," Control Engineering Practice, vol. 5, no. 5, pp. 671-682, 1997.
 J. Gertler, "Fault detection and isolation using parity relations," Control Engineering Practice, vol. 5, no. 5, pp. 653-661, 1997.
 R. Isermann, "Process fault detection based on modelling and estimation methods : A survey," Automatica, vol. 20, pp. 387-404, 1994.
 H. Paynter, Analysis and Design of Engineering Systems. MIT press, 1961.
 A. Mukerjee and A. Samantaray, "System modeling through bond graph objects on symbols 2000," in International Conference on bond graph Modeling and Simulation (ICBGM01), vol. 33 of Simulation series, pp. 164-170, 2001.
 G. Dauphin-Tanguy, A. Rahmani, and C. Sueur, "Formal determination of controllability/observability matrices for multivariables systems modelled by bond graph," in International IMACS/SILE Symposium on Robotics, Mechatronics and Manufacturing Systems92, pp. 573-578, 1992.
 G. Biswas, G. Simon, , N. Mahadevan, N. Narasimhan, S. Raminez, and G. Karsai, "A robust method for hybrid diagnosis of complex systems," in 5th IFAC symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS, pp. 1125-1131, 2003.
 A. K. Samantaray and B. Ould-bouamama, Model-based Process Supervision: A Bond Graph Approach. ISBN 978-1-84800-158-9, Springer- Verlag, 2008.
 B. Ould-bouamama, M. Staroswiecki, and A. Samantaray, "Software for supervision system design in process engineering industry," in 6th IFAC, SAFEPROCESS, (Beijing, China), pp. 691-695, 2006.
 Z. Shi, F. Gu, B. Lennox, and A. Ball, "The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system," Control Engineering Practice, vol. 13, pp. 1357-1367, 2005.
 J. Armengol, J. Vehi, M. A. Sainz, and P. Herrero, "Fault detection in a pilot plant using interval models and multiple sliding windows," in Safeprocess 2003 (e. N. Eva Wu, ed.), pp. 729-734, IFAC, 2003.
 M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes: Theory and Application. ISBN 0-13-126780-9, Prentice Hall, 1993.
 R. Wang, Statistical theory. Xian Jiaotong University Press China, 2003.
 M. A. Djeziri, B. O. Bouamama, and R. Merzouki, "Modelling and robust fdi of steam generator using uncertain bond graph model," Journal of Process Control, vol. 19, pp. 149-162, January 2009.
 P. Weber, D. Theilliol, C. Aubrun, and A. Evsukoff, "Increasing effectiveness of model-based fault diagnosis: a dynamic bayesian network design for decision making," in 6th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes SAFEPROCESS, 2006.
 J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, 1988.
 D. Koller and U. Lerner, Sequential Monte Carlo Methods in practice, ch. Sampling in Factored Dynamic Systems, pp. 445-464. Springer- Verlag, 2000.
 I. Roychoudhury, G. Biswas, and X. Koutsoukos, "A bayesian approach to efficient diagnosis of incipient faults," in 17th International Workshop on Principles of Diagnosis (DX 06), (Spain), pp. 243-250, 2006.
 P. Mosterman and G. Biswas, "Diagnosis of continuous valued systems in transient operating regions," IEEE Trans. on Systems, Man and Cybernetics, vol. 29, no. 6, pp. 554-565, 1999.
 P. J. Gawthrop, "Bicausal bond graphs," in ICBGM-95, pp. 83-88, Las Vegas, USA, 1995.
 B. Bregon, B. Belarmino, G. Biswas, and X. Koutsoukos, "Generating possible conflicts from bond graphs using temporal causal graphs," in 23rd European Conference on Modelling and Simulation ECMS09, pp. 675-682, June 9-12, Madrid, 2009.
 N. Mehranbod, M. Soroush, and C. Panjapornpon, "A method of sensor fault detection and identification," Journal of Process Control, vol. 15, pp. 321-339, 2005.
 M. Anderson, R. Anderson, and K. Wheeler, "Filtering in hybrid dynamic bayesian networks," in Interntional Conference on Acoustics, Speech and Signal Processing, vol. 5, pp. 773-776, 2003.
 X. Zhang and K. A. Hoo, "Effective fault detection and isolation using bond graph-based domain decomposition," Computers and Chemical Engineering, vol. 35, pp. 132-148, 2011.
 M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault Tolerant Control. Springer-Verlag, 2003.
 P. Declerck, Analyse structurelle et fonctionnelle des grands systmes : Application une centrale PWR 900 MW. PhD thesis, Universit des Sciences et Technologies de Lille, France, 1991.
 M. Tagina, Application de la Modlisation Bond Graph la Surveillance Des Systmes Complexes. PhD thesis, Universit des Sciences et Technologies de Lille, France, 1995.
 A. Aitouche and B. Ould-bouamama, "Sensor location with respect to fault tolerance properties," International Journal of Automation and Control, vol. 4, no. 3, pp. 298-316, 2010.
 C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan, "An introduction to mcmc for machine learning," Kluwer Academic Publishers, vol. 50, pp. 5-43, 2001.
 M. S. Hamada, A. G. Wilson, C. Reese, and H. Martz, Bayesian reliability. ISBN 978-0-387-77948-5, Springer series in statistics, 2008.
 W. R. Gilks and P. Wild, "Adaptive rejection sampling for gibbs sampling," Applied Statistics, vol. 41, pp. 337-348, 1992.
 G. Casella and E. I. George, "Explaining the gibbs sampler," American Statistician, vol. 46, pp. 167-174, August 1992.
 S. Lauritzen and F. Jensen, "Stable local computation with conditional gaussian distributions," Tech. Rep. R-99-2014, Dept. Math. Sciences, Aalborg Univ., 1999.
 U. Lerner, E. Segal, and D. Koller, "Exact inference in networks with discrete children of continuous parents," in Uncertainty in Artificial Intelligence (C. Morgan Kaufmann, San Francisco, ed.), vol. 17, pp. 319- 328, 2001.
 D. Koller, U. Lerner, and D. Angelov, "A general algorithm for approximate inference and its application to hybrid bayes nets," in the Fifteenth Annual Conf. on Uncertainty in Artificial Intelligence UAI-99, (Stockholm, Sweden), pp. 324-333, August 1999.
 A. V. Kozlov and D. Koller, "Nonuniform dynamic discretization in hybrid networks," Uncertainty in artificial intelligence, vol. 13, pp. 314- 325, 1997.
 S. Moral, R. Rumi, and A. Salmeron, "Mixtures of truncated exponentials in hybrid bayesian networks," in In Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, vol. 2143 of Lecture Notes in Artificial Intelligence, pp. 145-167, Springer-Verlag, 2001.
 U. Lerner, R. Parr, D. Koller, and G. Biswas, "Bayesian fault detection and diagnosis in dynamic systems," in 17th National Conference on Artificial Intelligence (AAAI), pp. 531-537, 2000.
 H. Rinne, The Weibull Distribution: A Handbook. ISBN 978-1-4200- 8743-7, CRC Press, 2008.
 B. Ould-bouamama, K. Medjaher, A. Samantaray, and M. Staroswiecki, "Supervision of an industrial steam generator part i and ii," Control Engineering Practice, vol. 14, pp. 71-83, 2006.
 N. S. Center, Handbook of Reliability Prediction Procedures for Mechanical equipment. No. NSWC-07, Carderock Division, September 28 2007.
 GeNie2.0, "Website , http://genie.sis.pitt.edu," 2011.