A standards-based approach to gray-scale health assessment using fuzzy fault trees

Abstract

As part of a project to examine how current standards focused on test and diagnosis might be extended to address requirements for prognostics and health management, we have been exploring alternatives for incorporating facilities to represent gray-scale health information in the IEEE Std 1232 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). In this work, we extend the AI-ESTATE Common Element Model to provide “soft outcomes” on tests and diagnoses. We then demonstrate how to use these soft outcomes with the AI-ESTATE Fault Tree Model to implement a “fuzzy” fault tree. The resulting model then enables isolating faults within a system such that levels of degradation can also be tracked. In this paper, we describe the proposed extensions to AI-ESTATE as well as how those extensions work to implement a fuzzy fault tree using the demonstration circuit from previous Automatic Test Markup Language (ATML) demonstrations.