P Arametric Identification for Robust Fault Detection

The w orkpresents some simulation results concerning the application of robust model{based fault diagnosis to an industrial process by using iden ti cation and disturbance de{coupling techniques. The rst step of the considered approach iden ti es sev eral equation error models b y means of the input{output data acquired from the monitored system. Each model describes the di erent w orkingconditions of the plant. In particular, the equation error term of the iden ti edmodels tak es into account disturbances (non{measurable inputs), non{linear and time{invarian t terms, measurement errors, etc. The next step of this method exploits state{space realization of the input{output equation error models allo wing to de ne several equivalen tdisturbance distribution matrices related to the error terms. Moreover, in order to achiev egood robustness properties for a process normally w orkingat di erent operating points, a single optimal equivalen t disturbance distribution matrix is selected. Finally, eigenstructure assignment method for robust residual generation and disturbance de{coupling can be successfully exploited for the fault diagnosis of the dynamic process. The fault diagnosis procedure is applied to a benchmark simulation of a gas turbine process.