Parametric Identification in Robust Fault Detection

The work presents some results concerning robust model--based fault diagnosis of a dynamic process using identification techniques. The first step of the considered approach estimates several equation error models by means of the input--output data acquired from the monitored system. Each model describes the different working condition of the plant. In particular, the equation error term of the identified models takes into account disturbances (non--measurable inputs), non--linear and time--invariant terms, measurement errors, etc. The next step of the method exploits state--space realization of the input--output equation error models allowing to define several equivalent disturbance distribution matrices related to the error terms. Moreover, in order to achieve good robustness properties for a process normally working at different operating points, a single optimal equivalent disturbance distribution matrix can be selected. Finally, the well--established eigenstructure assignment results for robust fault diagnosis can be successfully applied to a dynamic process.