Paper Info
Title
Use of Bayesian Networks for Qualification Planning: A Predictive Analysis Framework for a Technically Complex Systems Engineering Problem
Abstract
This paper discusses the viability of using Bayesian Network (BN) models to support qualification planning in order to predict the suitability of Six Degrees of Freedom (6DOF) vibration testing for qualification. Qualification includes environmental testing such as temperature, vibration, and shock to support a stochastic argument about the suitability of a design. Qualification is becoming more complex and restricted yet available new technologies are not fully utilized. Technology has advanced to the state where 6DOF vibration shakers and control systems capable of high frequency tests are possible, but the problem using these systems is far more complex than traditional single degree of freedom (SDOF) tests. This challenges systems engineers as they strive to plan qualification in an environment where technical, environmental, and political constraints are coupled with the traditional cost, risk and schedule constraints. New technologies are also available for systems engineers to combine technical understanding with cost, risk and schedule factors to aid in decision making for complex problems such as qualification planning. BN models may provide the framework to aid Systems Engineers in planning qualification efforts with complex constraints. This paper discusses related work, the current approach and results of this research.
Year
DOI
Venue
2015
10.1016/j.procs.2015.09.173
Procedia Computer Science
Keywords
Field
DocType
Bayesian network,qualification,vibration,systems engineering,6DOF,multi-axis,decision model
Complex system,Data mining,Computer science,Single degree of freedom,Six degrees of freedom,Bayesian network,Emerging technologies,Control system,Complex problems
Conference
Volume
ISSN
Citations 
61
1877-0509
1
PageRank 
References 
Authors
0.38
5
2
Name
Order
Citations
PageRank
Davinia B. Rizzo110.72
Mark Blackburn2456.57