Cart (Loading....) | Create Account
Close category search window
 

Component-based multi-model approach for fault detection and diagnosis of a centrifugal pump

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

4 Author(s)
Wolfram, A. ; Inst. of Autom. Control, Darmstadt Univ. of Technol., Germany ; Fussel, D. ; Brune, T. ; Isermann, Rolf

A model-based approach for fault detection and diagnosis of nonlinear processes is presented. However, the supervision of nonlinear systems is often very difficult in view of the lack of accurate models. Neuro-fuzzy models may help to cope with this problem since they can be trained from measured data. In this paper the application of a multi-model approach for fault detection and diagnosis of centrifugal pumps is presented. For this purpose the process is decomposed in several sub-processes. The supervision scheme allows the detection of several faults both in the hydraulic and mechanical subsystems

Published in:

American Control Conference, 2001. Proceedings of the 2001  (Volume:6 )

Date of Conference:

2001

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.