Robust Fault Diagnosis using Parallelotope-based Set-membership Consistency Tests

The paper proposes robust fault detection methods that take advantage of a recently proposed set-membership identification procedure based on parallelotopes for systems linear in the parameters. It is shown that consistency checks indicating faults can be performed in a natural manner with a parallelotope description of the feasible parameter set. Fault detection algorithms are presented for systems with invariant parameters, with parameter variation bounded between samples and with unbounded variation. Finally, an example is given which demonstrates how the algorithms work on a multivariable process.