By Topic

A neural networks based approach for fault detection and diagnosis: application to a real process

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

2 Author(s)
M. J. de la Fuente ; Dept. de Ingenieria de Sistemas y Autom., Valladolid Univ., Spain ; P. Vega

This paper proposes a new fault detection and diagnosis (FDD) method based on the online parameter estimation using the frequency contents of the signals and backpropagation neural networks. When a fault occurs the parameters in a nonlinear mathematical model of the process change. A method for detecting and tracking the different values of the parameters is proposed, which tries to be robust with respect to low frequency disturbances. The new FDD method together with a classical fault detection method are applied to a wastewater treatment plant, placed in Manresa, Spain. A set of real experiments are presented in order to compare and validate the methods in industrial applications

Published in:

Control Applications, 1995., Proceedings of the 4th IEEE Conference on

Date of Conference:

28-29 Sep 1995