We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Early Detection of System Failure in Complex Chemical or Non-Electrical Based Systems Using a Nerual Network

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

1 Author(s)
Stone, V.M. ; Univ. of New Mexico, Albuquerque

Health monitoring systems have evolved into complex diagnostic systems. Researchers are attempting to include prediction, or prognostics, into such systems and are resorting to hybrid systems fusing statistics, data mining, expert systems, neural networks, and more into system that perform not only health monitoring and diagnostics but prognostics as well. However, no work has been reported on systems based on chemical or other non-electrical processes where the interactions of the various operating parameters are subtle, complex, and correlated in unknown or difficult to elicit ways. This paper describes the use of neural networks to provide early detection of the onset of operational failure in such devices and suggests ways to use it as part of a prognostic system.

Published in:

Automation Congress, 2006. WAC '06. World

Date of Conference:

24-26 July 2006