APPLICATION OF A BAYESIAN NETWORK BASED FAILURE DETECTION AND DIAGNOSIS FRAMEWORK ON MARITIME DIESEL ENGINES

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dc.contributor.advisorGroth, Katrinaen_US
dc.contributor.authorReynolds, Stevenen_US
dc.contributor.departmentSystems Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-06-15T05:45:09Z
dc.date.available2022-06-15T05:45:09Z
dc.date.issued2022en_US
dc.description.abstractDiesel engine propulsion has been the largest driver of maritime trade and transportation since its development in the early 20th century and the technology surrounding the operation and maintenance of these systems has grown in complexity leading to rapid advancement in amount and variety of data being collected. This increase in reliability data provides a fantastic opportunity to improve upon the existing tools troubleshooting and decision support tool used within the maritime engine community to enable a more robust understanding of engine reliability. This work leverages this opportunity and applies it to the Coast Guard and its acquisition of the Fast Response Cutter (FRC) fleet powered by two MTU20V4000M93 engines integrated with top of line monitoring and control equipment.The purpose of this research is to create procedures for creating a Failure Detection and Diagnosis (FDD) model of a maritime diesel engine that updates existing Probabilistic Risk Analysis (PRA) data with input from the engine monitoring and control system using Bayesian inference. A literature review of existing work within the PRA and Prognostics and Health Management (PHM) fields was conducted with specific focus on the advancement and gaps in the field specific to their use in maritime engine applications. Following this, a hierarchal ruleset was created that outlines procedures for integrating existing PRA data and PHM metrics into a Bayesian Network structure. This methodology was then used to build a Bayesian Network based FDD model of the FRC engine. This model was then validated by Coast Guard Engineers and run through a diagnostic use case scenario demonstrating the model’s suitability in the diagnostic space.en_US
dc.identifierhttps://doi.org/10.13016/0ydz-ynmx
dc.identifier.urihttp://hdl.handle.net/1903/28787
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledNaval engineeringen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pquncontrolledBayesianen_US
dc.subject.pquncontrolledEngineen_US
dc.subject.pquncontrolledFailureen_US
dc.subject.pquncontrolledProbabalisticen_US
dc.subject.pquncontrolledPrognosticsen_US
dc.subject.pquncontrolledReliabilityen_US
dc.titleAPPLICATION OF A BAYESIAN NETWORK BASED FAILURE DETECTION AND DIAGNOSIS FRAMEWORK ON MARITIME DIESEL ENGINESen_US
dc.typeThesisen_US

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