By Topic

Real-time, embedded diagnostics and prognostics in advanced artillery 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
$31 $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

3 Author(s)

This paper explores an integrated modeling and reasoning approach to real-time, embedded diagnostics and prognostics called the Armament Diagnostic And Prognostic Tool (ADAPT). In addition, an approach for using the real-time diagnostic and prognostic information for degraded operation control of armament systems is described. The application focus of this paper is on advanced armament system gun mounts; however, the ADAPT approach has general applicability to a large class of complex systems. It is powered and enabled by the integration of three modeling and reasoning technologies Prognostics Framework (PF) model-based reasoning, Statistical Network (StatNet) modeling, and a time domain gun mount simulation. The model embodied in the PF reasoning is called a fault/symptom matrix, which is a connectivity matrix that represents the linkages of anomalies or faults (rows in the matrix) to observable measurements and the coverage of tests that pass or fail (columns in the matrix). StatNet is a modeling algorithm in the ModelQuest Analyst data mining tool. This algorithm combines the effective 'network of functions' concept in neural networks with proven statistical learning techniques.

Published in:

AUTOTESTCON Proceedings, 2002. IEEE

Date of Conference: