A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Austin D. Lewis,Katrina M. Groth

doi:10.3390/a13030064

Abstract

Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.

Highlights

Most industries depend heavily on the functionality of large and costly systems with tightly integrated hardware, software, and human components
The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent complex engineering system (CES) health
Certain relationships might not occur in an actual accident scenario, those elements still need to be included in the conditional probability table (CPT)

Summary

Introduction

Most industries depend heavily on the functionality of large and costly systems with tightly integrated hardware, software, and human components. Financial concerns, and industrial regulations require modeling and predicting the health state of such complex engineering systems (CESs). Dynamic Bayesian networks (DBNs), has shown the ability to infer complex time-dependent causal relationships between nodes connected within these models [1]. There has been growing interest in using DBNs to assess current CES health and model future system health. Applying causal-based reasoning to CES operational data has the potential to generate diagnostic system state models, as well as prognostic outlooks on the future health of the system or potential causes of system failure. There are still questions regarding how to make this prognostics and health management (PHM) process effective and efficient for the fast-paced cycle of industry operations and accident sequences

Methods

Results

Discussion

Conclusion