No abstract

With the increasing complexity of today's engineering systems that contain various component dependencies and degradation behaviors, there has been increasing interest in on-line System Health Management (SHM) capability to continuously monitor (via sensors and other methods of observation) system software, and hardware components for detection and diagnostic of safety-critical systems.

No 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.