The Ph.D. thesis aimed to define a comprehensive methodology for actuator, component and sensor fault detection and isolation by using an output estimation approach. The method is based on residual processing schemes, which include a simple threshold detection, in deterministic case, as well as statistical analysis when data are affected by noise. The final result consists of a fault detection and isolation strategy based on diagnostic methods to generate redundant residuals analytically. A number of strategies for the design of residual generators are then proposed. The method proposed do not require any physical knowledge of the processes under observation since the mathematical description of the monitored system is obtained by means of a system identification scheme based on equation error and errors--in--variables models. It is worthy to note how this work presents a novel point of view of the model-based fault diagnosis. The new aspect consists of exploiting linear system identification procedures in connection with the model-based residual generation problem. The diagnostic tools presented in this thesis are well illustrated using practical application examples and the results show the effectiveness of the technique developed.
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