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.
Jan 22, 2021
Jan 22, 2021
|Neural network evaluation of model-based residuals in fault detection of time delay systems||Jan 22, 2021|
Huk, Maciej Korbicz, Józef - red. Uciński, Dariusz - red.
Emirsajłow, Zbigniew Korbicz, Józef - red. Uciński, Dariusz - red.
Martins, Valérie Santos dos Rodrigues, Mickael Diagne, Mamadou Korbicz, Józef - red. Uciński, Dariusz - red.
Gocławski, Jarosław Sekulska-Nalewajko, Joanna Kuźniak, Elżbieta Korbicz, Józef - red. Uciński, Dariusz - red.
Hui, Stefen Żak, Stanisław H. Beliczyński, Bartłomiej - red.
Tong, Shaocheng Yang, Gengjiao Zhang, Wei Korbicz, Józef - red. Uciński, Dariusz - red.
Kaliszuk, Joanna Greinert, Andrzej - red.