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dc.creatorFant, Earnest William
dc.date.available2011-02-18T21:17:49Z
dc.date.issued1987-08
dc.identifier.urihttp://hdl.handle.net/2346/15883en_US
dc.description.abstractThis research investigated life cycle reliability modeling in the early design stages. It was assumed that life cycle reliability modeling can be useful for the evaluation of reliability goals. In situations where new technology or a major change in application and/or in environment of a product are involved, the usual analysis techniques cannot compensate for a gross lack of data. The ability to model qualitative information about a product in a simple, straight-forward manner becomes a necessity. As quantitative data becomes available, the ability to update the qualitative model becomes an important requirement, thus allowing for the possible use of Bayesian analysis. To demonstrate the need for reliability assurance efforts as early as possible in the design process of a new technology product, a life cycle reliability, sensitivity analysis technique was developed. This technique produces results, using extremely limited data, to aid in establishing reliability goals. A Monte Carlo analysis system/subsystem level model and modeling procedure were developed, based on a stratified Monte Carlo sampling procedure called Latin hypercube sampling, which is capable of generating rank correlated random variates to induce dependences in the model. The Monte Carlo analysis was developed around an exponential failure model. The life cycle of a system was represented by a series arrangement of phases. Input into the model was in the form of mean time to failure distribution estimates and corresponding distribution estimates for the time in a life cycle phase. Algorithms were developed to fit various distributions to three estimates for mean time to failure and time in phase, similar to those used in project management/activity scheduling. Sensitivity analyses were performed to obtain an idea of how well a system must perform over its life cycle phases in order to fulfill a given life cycle reliability requirement. The results obtained from the modeling were used to assess possible reliability characteristics of a generic space based pulsed power system.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.subjectBayesian statistical decision theoryen_US
dc.subjectProduct life cycleen_US
dc.subjectMonte Carlo methoden_US
dc.subjectReliability (Engineering) -- Statistical methodsen_US
dc.titleMonte Carlo analysis of life cycle reliability compatible with Bayesian analysis
dc.typeDissertation
thesis.degree.namePh.D.
thesis.degree.levelDoctoral
thesis.degree.disciplineIndustrial and Systems Engineering
thesis.degree.grantorTexas Tech University
thesis.degree.departmentIndustrial and Systems Engineering
thesis.degree.departmentIndustrial Engineering
dc.degree.departmentIndustrial and Systems Engineeringen_US
dc.rights.availabilityUnrestricted.


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