Dissertation/Thesis Abstract

A Predictive Analysis Framework for Six Degrees of Freedom Vibration Qualification
by Rizzo, Davinia B., Ph.D., Stevens Institute of Technology, 2017, 630; 10606844
Abstract (Summary)

As systems become more complex, systems engineers rely on experts to inform decisions. There are few experts and limited data in many complex new technologies. This challenges systems engineers as they strive to plan activities such as qualification in an environment where technical constraints are coupled with the traditional cost, risk and schedule constraints. Bayesian Network (BN) models may provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and using available data. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision as well as with information on driving factors. This dissertation discusses the research resulting in a BN model designed to aid qualification decisions based primarily on expert knowledge supplemented with limited data. The goal of the research is to explore the viability of using BN models to aid systems engineers in planning qualification efforts by predicting the suitability of Six Degrees of Freedom (6DOF) vibration testing for qualification. The particular BN model created in this research incorporates deeply technical factors into the decision space. As the 6DOF high frequency test industry has less than 50 data sets, model creation relies primarily on experts and elicitation methods are critical in the research. Validation of the model includes historical data, current data, and expert peer review and validation test cases. The model proved to effectively aid decisions and correctly identified driving factors. Additionally, the model had an unexpected result of accelerating learning in engineers not familiar with the 6DOF problem space. I discuss research results, contributions to the present state of knowledge in the field, and the potential future work in this dissertation.

Indexing (document details)
Advisor: Blackburn, Mark
Commitee: Croessmann, Dennis, Prasad, Marehalli, Wade, Jon
School: Stevens Institute of Technology
Department: Systems and Enterprises
School Location: United States -- New Jersey
Source: DAI-B 79/08(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Aerospace engineering, Mechanical engineering, Systems science
Keywords: Bayesian model, Decision aid, Qualification, Six degrees of freedom, Systems engineering, Vibration
Publication Number: 10606844
ISBN: 9780355743340
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