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Abstract:
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The development of system identification and fault diagnosis theory is of great practical significance . Systems are concerned with a broad spectrum of human -made machinery , including industrial production facilities (power plants , chemical plants , oil refinery , semiconductor fabrication plants , steel mills , paper mills , etc . ) , transportation vehicles (ships , airplanes , automobiles ) and household appliances (heating /air conditioning equipment , refrigerators , washing machines , etc . ) . This dissertation is focused on subspace identification algorithms and optimal structured residuals approach for processes modeling and diagnosis .
Main contributions of this work include : (1 ) Novel subspace identification methods (SIMs ) with enforced causal models are implemented . It has been shown that proposed algorithm has lower estimation variance compared to traditional SIMs . Meanwhile the rigorous analysis shows that the proposed algorithms are consistent under certain assumptions . (2 ) The feasibility of closed -loop subspace identification is investigated . Novel closed -loop subspace identification methods with innovation estimation are proposed . The new algorithms are shown to be consistent under closed -loop conditions , while the traditional SIMs fail to provide consistent estimates . (3 ) A new optimal structured residuals (OSR ) approach for unidirectional fault diagnosis is proposed . The necessary and sufficient conditions for unidirectional fault isolability with OSR approach are introduced . (4 ) The OSR for unidirectional fault diagnosis is extended to multidimensional fault diagnosis . The sufficient condition for deterministic multidimensional fault isolability is investigated . |