Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Chonlagarn Iamsumang Ali Mosleh Mohammad Modarres

Abstract

This paper presents a new dependency computational algorithm for reliability inference with dynamic hybrid Bayesian network. It features a component-based algorithm and structure to represent complex engineering systems characterized by discrete functional states (including degraded states), and models of underlying physics of failure, with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using pre-computation and dynamic programming for real-time monitoring of system health. The scope of this research includes new modeling approach, computation algorithm, and an example application for on- line System Health Management.

How to Cite

Iamsumang, C. ., Mosleh, A. ., & Modarres, M. . (2014). Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2422
Abstract 20 | PDF Downloads 17

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
Boudali, H., & Dugan, J. B. (2006). A continuous-time Bayesian network reliability modeling, and analysis framework. Reliability, IEEE Transactions on, 55 (1), 86-97.

Boyen, X., & Koller, D. (1998). Tractable inference for complex stochastic processes. Proceedings of the fourteenth conference on uncertainty in artificial intelligence.

Chen, Z. (2003). Bayesian filtering: From Kalman filters to particle filters, and beyond. Statistics, 1-69.

Cooper, G. F. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42 (2-3), 393-405.

Cousins, S. B., Chena, W., & Frisse, M. E. (1993). A tutorial introduction to stochastic simulation algorithms for belief networks. Artificial Intelligence in Medicine, 5 (4), 315-340.

Dagum, P., & Horvitz, E. (1993). A Bayesian analysis of simulation algorithms for inference in belief networks. Networks, 23 (5), 499-516.

Dagum, P., & Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60 (1), 141-154.

Doguc, O., & Ramirez-Marquez, J. E. (2009). A generic method for estimating system reliability using Bayesian networks. Reliability Engineering & System Safety, 94 (2), 542-550.

Ferreiro, S., Arnaiz, A., Sierra, B., & Irigoien, I. (2011). A Bayesian network model integrated in a prognostics and health management system for aircraft line maintenance. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering , 225 (8), 886-901.

Friedman, N. (1998). The Bayesian structural EM algorithm. In G. F. Cooper (Ed.), Proceedings of the fourteenth conference on uncertainty in intelligence (UAI-98) (pp. 129-138). Kaufmann.

Jensen, F. (2001). Bayesian Networks and Decision Graph. Springer.

Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering & System Safety, 92 (1), 92-108.

Lauritzen, S. L., & Jensen, F. (2001). Stable local computation with conditional Gaussian distributions. Stat. & Comp., 11, 191-203.

Lerner, U. N. (2002). Hybrid Bayesian networks for reasoning about complex systems. Standford University, Dep. of Comp. Sci. Stanford.

Moral, S., Rumi, R., & Salmeron, A. (2001). Mixtures of truncated exponentials in hybrid Bayesian networks. ECSQARU 2001. LNCS (LNAI) (Vol. 2143, pp. 156- 167). Springer, Heidelberg.

Murphy, K. (2002). Dynamic Bayesian networks: representation, inference and learning. PhD thesis, UC Berkeley, Dept. Computer Science.

Neil, M., Tailor, M., Marquez, D., Fenton, N., & Hear. (2007). Inference in Bayesian networks using dynamic discretisation. Statistics and Computing, 17 (3), 219- 233.

Pearl, J. (1986). Fusion, propagation and structuring in belief networks. Artificial Intelligent, 29, 241-288.

Schumann, J., Rozier, K. Y., Reinbacher, T., Mengshoel, O. J., Mbaya, T., & Ippolito, C. (2013). Towards real-time, on-board, hardware-supported sensor and software health management for unmanned aerial systems. Annual conference of the prognostics and health managements society.

Shenoy, P. P. (2006). Inference in hybrid Bayesian networks using mixtures of Gaussians. Uncertainty in Artificial Intelligence (pp. 428-436). AUAI Press, Corvallis.

Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012). A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. Reliability, IEEE Transactions on, 61 (2), 491-503.

Weber, P., & Jouffe, L. (2006). Complex system reliability modelling with dynamic object oriented Bayesian networks (DOOBN). Reliability Engineering & System Safety, 91 (2), 149-162.

Wilson, A. G., & Huzurbazar, A. V. (2007). Bayesian networks for multilevel system reliability. Reliability Engineering & System Safety, 92 (10), 1413-1420.
Section
Technical Papers