Probabilistic Safety Assessment and Management PSAM 12, June 2014, Honolulu, Hawaii Degradation Modeling and Algorithm for On-line System Health Management using Dynamic Hybrid Bayesian Network
نویسندگان
چکیده
This paper presents a new modeling method and 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. In System Health Management applications, this method introduces a well-defined interface between continuous system component status and discrete system functionality within the network model. 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.
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