Robust residual generation for model-based fault diagnosis of dynamic systems.

To guarantee safe operation and mission completion, any fault in an automatic system has to be diagnosed as early as possible. Model-based techniques have been widely recognized as feasible and powerful approaches for diagnosing faults and require a mathematical model of the monitored system. A prerequisite for successful model-based fault diagnosis is satisfactory robustness with respect to modelling uncertainties. This thesis examines and develops further the theory and application of robust residual generation techniques in model-based fault diagnosis, beginning with a study and review of basic principles of model-based fault diagnosis. A number of strategies for the design of robust residual generators are then proposed. The thesis proposes a new full-order unknown input observer structure for robust residual generation and this structure is then used to design directional and minimum variance residuals. This is followed by a very thorough presentation of the eigenstructure assignment approach to fault diagnosis. A new algorithm to assign right observer eigenvectors in disturbance de-coupling design is presented. The disturbance decoupling residual generation is then used for diagnosing faults in a jet engine system example. To facilitate this application, several techniques are proposed to derive an approximate disturbance distribution matrix. These techniques enlarge the application domain of disturbance de-coupling residual generation approaches. Robust residual generation can be treated as a multi-objective optimization problem in which fault sensitivity is to be maximized, whilst the sensitivity to modeffing uncertainties is to be minimized. The thesis defines a number of performance indices in observer-based residual generation and the multi-objective optimization is solved by a combination of the method of inequalities and genetic algorithms. Finally, the thesis studies the design of optimally robust parity relations using multi-criterion optimization. The techniques developed in this thesis are well illustrated using either academic or practical application examples and the results show the effectiveness of the developed techniques.

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