On-line fault diagnosis of dynamic systems via robust parameter estimation

A procedure simultaneously achieving the detection of faults, their location and their identification is presented. The systems considered are MISO systems represented by ARX models, the parameters of which are estimated on-line by a robust procedure. A priori knowledge of the faults which can occur is used. The faults modelled here are outliers, biases or drifts, and can act upon output, inputs or even noise. The magnitude of a fault is estimated on a moving window from the prediction error sequence by least squares. Statistical tests of the significance of the estimated parameters corresponding to the different faults are performed. An application on the strip drive in the furnace of an annealing line is finally presented.