By Topic

Inference-Based Decentralized Prognosis in Discrete Event Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Shigemasa Takai ; Division of Electrical, Electronic and Information Engineering, Osaka University, Suita, Japan ; Ratnesh Kumar

For discrete event systems, we study the problem of predicting failures prior to their occurrence, also referred to as prognosis, in the inference-based decentralized framework where multiple decision-makers interact to make the global prognostic decisions. We characterize the class of systems for which there are no missed detections (all failures can be prognosed prior to their occurrence) and no false alarms (all prognostic decisions are correct) by introducing the notion of -inference-prognosability, where the parameter represents the maximum ambiguity level of any winning prognostic decision. An algorithm for verifying -inference-prognosability is presented. We also show that the notion of coprognosability introduced in our prior work is the same as 0-inference-prognosability, and as the parameter is increased, a larger class of prognosable systems is obtained.

Published in:

IEEE Transactions on Automatic Control  (Volume:56 ,  Issue: 1 )