Paper Info
Title
Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem
Abstract
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 provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and incorporating technical factors. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision supplemented with information on driving factors. This allows a systems engineer to make informed decisions and examine what-if scenarios. This paper discusses a novel process developed to define a BN model structure based primarily on expert knowledge supplemented with extremely limited data (25 data sets or less). The model was developed to aid qualification decisionsspecifically to predict the suitability of six degrees of freedom (6DOF) vibration testing for qualification. The process defined the model structure with expert knowledge in an unbiased manner. Validation during the process execution and of the model provided evidence the process may be an effective tool in harnessing expert knowledge for a BN model.
Year
DOI
Venue
2018
10.1002/sys.21431
SYSTEMS ENGINEERING
Keywords
Field
DocType
Bayesian network,decision model,qualification,structural knowledge assessment
Economics,Financial economics,Operations research,Bayesian network,Decision model,Qualification problem,Vibration,Data model
Journal
Volume
Issue
ISSN
21.0
4.0
1098-1241
Citations 
PageRank 
References 
1
0.36
7
Authors
2
Name
Order
Citations
PageRank
Davinia B. Rizzo110.36
Mark Blackburn2456.57