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

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  • Lewis, Austin D.
  • Groth, Katrina M.

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.

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  • Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:reensy:v:236:y:2023:i:c:s0951832023001217
    DOI: 10.1016/j.ress.2023.109206
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