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Title: Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem

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 decisions—specifically 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. Finally, validation during the process execution and of the model provided evidence the process may be an effective tool in harnessingmore » expert knowledge for a BN model.« less
Authors:
ORCiD logo [1] ;  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Stevens Inst. of Technology, Hoboken, NJ (United States)
Publication Date:
Report Number(s):
SAND-2018-3223J
Journal ID: ISSN 1098-1241; 661799
Grant/Contract Number:
AC04-94AL85000; NA0003525
Type:
Accepted Manuscript
Journal Name:
Systems Engineering
Additional Journal Information:
Journal Volume: 21; Journal Issue: 4; Journal ID: ISSN 1098-1241
Publisher:
Wiley
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Bayesian network; decision model; qualification; structural knowledge assessment
OSTI Identifier:
1432479

Rizzo, Davinia B., and Blackburn, Mark R.. Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem. United States: N. p., Web. doi:10.1002/sys.21431.
Rizzo, Davinia B., & Blackburn, Mark R.. Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem. United States. doi:10.1002/sys.21431.
Rizzo, Davinia B., and Blackburn, Mark R.. 2018. "Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem". United States. doi:10.1002/sys.21431.
@article{osti_1432479,
title = {Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem},
author = {Rizzo, Davinia B. and Blackburn, Mark R.},
abstractNote = {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 decisions—specifically 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. Finally, 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.},
doi = {10.1002/sys.21431},
journal = {Systems Engineering},
number = 4,
volume = 21,
place = {United States},
year = {2018},
month = {3}
}