Using continuous-time Bayesian networks for standards-based diagnostics and prognostics

Author

Perreault, Logan ; Sheppard, John ; King, H. ; Sturlaugson, Liessman

Author_Institution

Dept. of Comput. Sci., Montana State Univ., Bozeman, MT, USA

fYear

2014

fDate

15-18 Sept. 2014

Firstpage

198

Lastpage

204

Abstract

In this paper we present a proposal for a new prognostic model to be included in a future revision of the IEEE Std 1232-2010 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). Specifically, we introduce the continuous time Bayesian network (CTBN) as an alternative to the previously proposed dynamic Bayesian network to provide an additional model for prognostic reasoning. We specify a semantic model capable of representing a CTBN within the standard and discuss the advantages of using such a model for prognosis. As with previous work, we demonstrate the feasibility and necessity of incorporating prognostic capabilities into the standard.

Keywords

belief networks; AI-ESTATE; CTBN; IEEE Std 1232-2010 standard; artificial intelligence exchange; continuous time Bayesian networks; dynamic Bayesian network; prognosis; prognostic model; prognostic reasoning; semantic model; standards-based diagnostics; Bayes methods; Cognition; Data models; Markov processes; Prognostics and health management; Random variables; Standards;

fLanguage

English

Publisher

ieee

Conference_Titel

AUTOTESTCON, 2014 IEEE

Conference_Location

St. Louis, MO

Print_ISBN

978-1-4799-3389-1

Type

conf

DOI

10.1109/AUTEST.2014.6935145

Filename

6935145