By Topic

A forecasting model to equipment health status based on PSR&Elman technology

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

4 Author(s)
Yuefeng Chen ; 63963 units and 66393 units, The fourth department, Beijing, China ; Yuansheng Dong ; Huting Song ; Feng Liu

It plays a crucial role in autonomic logistics or maintenance decision-making on condition to forecast equipment health status. However it was influenced by many various factors with complexity as variable, strong coupling, nonlinear and dynamic. The difficulty to forecast equipment health status lies in treating time sequence characteristic of health status index and complexity characteristic of equipment system which need a dynamic technology to map its inner status. Fresh technology of phase space reconstruction and Elman neural network were introduced. Equipment health status index was reconstructed in the phase space technology and the forecasting model was built up with dynamic neural network. The application case on this model was carried out with forecasting equipment accelerating time. The result shows an effective approach was explored to this problem.

Published in:

Reliability, Maintainability and Safety (ICRMS), 2011 9th International Conference on

Date of Conference:

12-15 June 2011