By Topic

Fault detection and identification using a hierarchical neural network-based system

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)
Abdel Mageed, M.F. ; Dept. of Electr. Eng., Cairo Univ., Giza, Egypt ; Sakr, A.F. ; Bahgat, A.

A new approach to detect and identify faults in complex processes is proposed. The approach is based on a hierarchical neural network structure. Other neural network applications in process fault diagnostics provide only fault detection and isolation. Through the proposed scheme, fault detection, isolation, and identification (recognizing the size of fault) can be achieved. This is due to the higher learning ability of the hierarchical structure. The performance of the suggested fault detector and identifier is evaluated via an industrial case study. The results show a satisfactory level of accuracy

Published in:

Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON '93., International Conference on

Date of Conference:

15-19 Nov 1993