A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods

Austin D. Lewis and Katrina M. Groth

Reliability Engineering and System Safety, 2023, vol. 236, issue C

Abstract: The energy and industry sectors depend upon the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to monitor system health and make informed decisions on maintenance and risk management practices. One proposed approach is to use causal-based models such as Dynamic Bayesian Networks (DBN), which contain the structural logic of and provide graphical representations of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing underlying conditional probability tables (CPTs) impact the DBN’s system health estimates.

Keywords: Time discretization; Dynamic Bayesian networks; Complex engineering systems; Prognostics and health management; Risk management (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:236:y:2023:i:c:s0951832023001217

DOI: 10.1016/j.ress.2023.109206

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