Abstract
This paper addresses the development and application of a data-driven method to the fault diagnosis in imperial smelting furnace (ISF). Based on the method of the weighted least squares vector machines regression, a Hammerstein model is constructed and identified for the ISF. This model is used to predict the dynamic behavior of the furnace and the possible faults in the process. The simulation study shows that the identified model well adapts to the changes in the structural parameters and provides accurate prediction.
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