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dc.contributor.authorSchupbach, Jordan
dc.contributor.authorPryor, Elliott
dc.contributor.authorWebster, Kyle
dc.contributor.authorSheppard, John
dc.identifier.citationSchupbach, J., Pryor, E., Webster, K., & Sheppard, J. (2022, August). Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling. In 2022 IEEE AUTOTESTCON (pp. 1-8). IEEE.en_US
dc.description.abstractThe problem of performing general prognostics and health management, especially in electronic systems, continues to present significant challenges. The low availability of failure data, makes learning generalized models difficult, and constructing generalized models during the design phase often requires a level of understanding of the failure mechanism that elude the designers. In this paper, we present a new, generalized approach to PHM based on two commonly available probabilistic models, Bayesian Networks and Continuous-Time Bayesian Networks, and pose the PHM problem from the perspective of risk mit-igation rather than failure prediction. We describe the tools and process for employing these tools in the hopes of motivating new ideas for investigating how best to advance PHM in the aerospace industry.en_US
dc.rightscopyright IEEE 2022en_US
dc.subjectbayesian networksen_US
dc.subjectdiagnostic modelingen_US
dc.subjectprognostic modelingen_US
dc.titleCombining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modelingen_US
mus.citation.journaltitle2022 IEEE AUTOTESTCONen_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US

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