A Bayesian Framework for Analysis of Pseudo-spatial Models of Comparable Engineered Systems With Application to Spacecraft Anomaly Prediction Based on Precedent Data

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dc.contributor.advisorMosleh, Alien_US
dc.contributor.advisorModarres, Mohammaden_US
dc.contributor.authorNdu, Obibobi Kamtochukwuen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-01-25T06:33:04Z
dc.date.available2018-01-25T06:33:04Z
dc.date.issued2017en_US
dc.description.abstractTo ensure that estimates of risk and reliability inform design and resource allocation decisions in the development of complex engineering systems, early engagement in the design life cycle is necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept development is the limited amount of high fidelity design and failure information available on the actual system under development. Applying the human ability to learn from experience and augment our state of knowledge to evolve better solutions mitigates this limitation. However, the challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets of generalization, Bayesian inference, and probabilistic risk analysis. We introduce an integrated framework for inferring the reliability, or other probabilistic measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our framework is based on learning from the performance of precedent designs and then applying the acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process. This dissertation presents a method for inferring properties of the conceptual variant using a pseudo-spatial model that describes the spatial configuration of the family of systems to which the concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial model based on rank-ordered subjective expert perception of design similarity between systems that elucidate the psychological space of the family. By a novel extension of Kriging methods for analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a Bayesian inference model that allows prediction of the probabilistic measure of interest.en_US
dc.identifierhttps://doi.org/10.13016/M2GB1XJ62
dc.identifier.urihttp://hdl.handle.net/1903/20421
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledAerospace engineeringen_US
dc.subject.pquncontrolledBayesian Inferenceen_US
dc.subject.pquncontrolledProbabilisitic Risk Modelingen_US
dc.subject.pquncontrolledReliability Analysisen_US
dc.subject.pquncontrolledSimilarity Quantificationen_US
dc.subject.pquncontrolledSpatial Process Modelingen_US
dc.titleA Bayesian Framework for Analysis of Pseudo-spatial Models of Comparable Engineered Systems With Application to Spacecraft Anomaly Prediction Based on Precedent Dataen_US
dc.typeDissertationen_US

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