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Radial basis function (RBF) neural networks are investigated here for process fault diagnosis. The use of the output prediction error, between a neural network model and a non-linear dynamic process, as a residual for diagnosing actuator, component and sensor faults is analysed. It is found that this residual for a dependent neural model is less sensitive to sensor faults than actuator or component...
This paper describes the validation of a model library for the simulation of drum boilers on the basis of static and dynamic experimental data obtained from a small-scale plant. All the steps of the validation process are described in detail, with particular reference to the modelling principles, to the trade-off between model complexity and accuracy, to the solution strategy and to the data-reconciliation...
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