Dynamic Bayesian Network Data Updating Approaches for Enabling Causal Prognostics and Health Management of Complex Engineering Systems

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Complex engineering systems (CESes), such as nuclear power plants or manufacturing plants, are critical to a wide range of industries and utilities; as such, it is important to be able to monitor their system health and make informed decisions on maintenance and risk management practices. However, currently available system-level monitoring approaches either ignore complex dependencies in their probabilistic risk assessments (PRA) or are prognostics and health management (PHM) techniques intended for simpler systems. The gap in CES health management needs to be closed through the development of techniques and models built from a systematic integration of PHM and PRA (SIPPRA) approach that considers a system's causal factors and operational context when generating health assessments.

The following dissertation describes a concentrated study that addresses one of the challenges facing SIPPRA: how to appropriately discretize a CES's operational timeline derived from multiple data streams to create discrete time-series data for use as model inputs over meaningful time periods. This research studies how different time scales and discretization approaches impact the performance of dynamic Bayesian Networks (DBNs), models that are increasingly used for causal-based inferences and system-level assessments, specifically built for SIPPRA health management. The impact of this research offers new insight into how to construct such DBNs to better support system-level health management for CESes.