Creator:
Zítek, Pavel ; Mánková, Renata ; Hlava, Jaroslav
Contributor:
Korbicz, Józef - red. ; Patton, Ronald J. - red.
Title:
Neural network evaluation of model-based residuals in fault detection of time delay systems
Subtitle:
Group publication title:
Subject and Keywords:
model-based fault detection ; anisochronic model ; state observer ; internal model control ; artificial neural networks
Abstract:
Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. ; Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). ; This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Pages:
Source:
AMCS, volume 9, number 3 (1999) ; click here to follow the link