Innovation generation in the presence of unknown inputs : application to robust failure detection
Résumé
The first step in innovations-based failure detection is the construction of an innovations generator, i.e., a filter which, in the absence of failures, from the inputs and the outputs of the system, generates a zero-mean white process with known covariance called innovations. Decision on whether a failure has occurred is then made by monitoring and applying statistical tests to this innovations process. In this paper, we present a method for constructing innovations in the case where the model contains unknown inputs and disturbances. Our solution is complete in the sense that it covers all "singular" cases.