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Abstract:
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This dissertation addresses fault detection and isolation (FDI ) for nonlinear systems based on models using two different approaches . The first approach detects and isolates single and multiple faults , particularly when there are restrictions in measuring process variables . The FDI model -based method is based on nonlinear state estimators , in which the estimates are calculated under high filtering , and a high fidelity residuals model , obtained from the difference between measurements and estimates . In the second approach , a robust fault detection and isolation (RFDI ) system , that handles both parameter estimation and parameters with uncertainties , is proposed in which complex models can be simplified with nonlinear functions so that they can be formulated as differential algebraic equations (DAE ) . In utilizing this framework , faults are identified by performing a statistical analysis . Finally , comparisons with existing data -driven approaches show that the proposed model -based methods are capable of distinguishing a fault from the diverse array of possible faults , a common occurrence in complex processes . |