PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Austin Lewis Co-author(s): Katrina M. Groth (kgroth@umd.edu)

Impact of Complex Engineering System Data Stream Discretization Techniques on the Performance of Dynamic Bayesian Network-Based Health Assessments
Critical infrastructure in the energy and industry sectors is dependent on the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to be able to monitor their system health and make informed decisions on maintenance and risk management practices. One proposed approach is the use of a causal-based model such as a Dynamic Bayesian Network (DBN) that contains the structural logic of and provides a graphical representation of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing the underlying conditional probability tables (CPTs) impact the DBN's system health estimates. Using a range of metrics designed for comparing health management models, this paper demonstrates the impact that different time discretization strategies have on the performance of DBN models built for CES health assessments. Using simulated nuclear data of a sodium fast reactor (SFR) experiencing a transient overpower (TOP), different strategies for discretizing CES data streams are used to construct the CPTs for a health-based DBN model. This leads to different models determining different assessments of overall system health. By understanding how these design factors impact the model’s health assessments, future risk models can be developed to provide a more meaningful assessment of a system’s health, resulting in more informed decisions.

Impact of Complex Engineering System Data Stream Discretization Techniques on the Performance of Dynamic Bayesian Network-Based Health Assessments

Critical infrastructure in the energy and industry sectors is dependent on the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to be able to monitor their system health and make informed decisions on maintenance and risk management practices. One proposed approach is the use of a causal-based model such as a Dynamic Bayesian Network (DBN) that contains the structural logic of and provides a graphical representation of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing the underlying conditional probability tables (CPTs) impact the DBN's system health estimates. Using a range of metrics designed for comparing health management models, this paper demonstrates the impact that different time discretization strategies have on the performance of DBN models built for CES health assessments. Using simulated nuclear data of a sodium fast reactor (SFR) experiencing a transient overpower (TOP), different strategies for discretizing CES data streams are used to construct the CPTs for a health-based DBN model. This leads to different models determining different assessments of overall system health. By understanding how these design factors impact the model’s health assessments, future risk models can be developed to provide a more meaningful assessment of a system’s health, resulting in more informed decisions.

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

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