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Title:

Neural network evaluation of model-based residuals in fault detection of time delay systems

Subtitle:

.

Group publication title:

AMCS, volume 9 (1999)

Creator:

Zítek, Pavel ; Mánková, Renata ; Hlava, Jaroslav

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

Contributor:

Patton, Ronald J. - red. ; Korbicz, Józef - red.

Date:

1999

Resource Type:

artykuł

Pages:

599-617

Source:

AMCS, volume 9, number 3 (1999)

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego