Modeling diagnostic constraints with AI-ESTATE

Author

Sheppard, John W. ; Astrand, Jonas

Author_Institution

ARINC Res. Corp., Annapolis, MD, USA

fYear

1995

fDate

8-10 Aug. 1995

Firstpage

528

Lastpage

533

Abstract

The Artificial Intelligence and Expert System Tie to Automatic Test Equipment (AI-ESTATE) subcommittee of the IEEE Standards Coordinating Committee 20 (SCC20) has been developing a set of standards for exchanging diagnostic knowledge in intelligent test systems. To date, AI-ESTATE has developed models for fault trees and enhanced diagnostic inference models (EDIMs). Since the start of committee work, it was believed that AI-ESTATE needed to address the issue of defining constraint knowledge, which could be used to guide and refine diagnostics. In this paper, we discuss early efforts by AI-ESTATE to define such a constraint model.

Keywords

IEEE standards; automatic test equipment; automatic test software; constraint handling; diagnostic expert systems; model-based reasoning; AI-ESTATE; ATE; IEEE standards; constraint model; constraint-based reasoning; diagnostic constraints; enhanced diagnostic inference models; fault tree models; intelligent test systems; interchange formats; logical constraints; temporal constraints; Artificial intelligence; Automatic test equipment; Automatic testing; Fault trees; Intelligent systems; Power system modeling; Standards Coordinating Committees; Standards development; System testing; USA Councils;

fLanguage

English

Publisher

ieee

Conference_Titel

AUTOTESTCON '95. Systems Readiness: Test Technology for the 21st Century. Conference Record

Conference_Location

Atlanta, GA, USA

Print_ISBN

0-7803-2621-0

Type

conf

DOI

10.1109/AUTEST.1995.522720

Filename

522720