A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework

TLDR

Dynamic systems exhibit complex behaviors where the sequence of failures, not just their combination, matters, and CTBNs use basic Bayesian network constructs to capture component behaviors and interactions similar to dynamic fault trees. The authors present a continuous‑time Bayesian network framework for dynamic systems reliability modeling and analysis. The framework combines basic Bayesian network constructs in a structured, modular, hierarchical fashion, enabling reliability, sensitivity, and uncertainty analyses. All analyses yield closed‑form solutions.

Abstract

We present a continuous-time Bayesian network (CTBN) framework for dynamic systems reliability modeling and analysis. Dynamic systems exhibit complex behaviors and interactions between their components; where not only the combination of failure events matters, but so does the sequence ordering of the failures. Similar to dynamic fault trees, the CTBN framework defines a set of 'basic' BN constructs that capture well-defined system components' behaviors and interactions. Combining, in a structured way, the various 'basic' Bayesian network constructs enables the user to construct, in a modular and hierarchical fashion, the system model. Within the CTBN framework, one can perform various analyses, including reliability, sensitivity, and uncertainty analyses. All the analyses allow the user to obtain closed-form solutions.