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This paper addresses the identification of non-linear systems. A wide class of these systems can be described using nonlinear\ud time-invariant regression models, that can be approximated by means of piecewise a ne prototypes with an\ud arbitrary degree of accuracy. This work concerns the identi®cation of piecewise affine model parameters through\ud input-output data affected by additive noise. In order to show the e ectiveness of the developed technique, the results\ud obtained in the identification of both a simple simulated system and a real dynamic process are reported

In this paper a model-based procedure exploiting analytical redundancy for the detection and isolation of faults in input-output control sensors of a dynamic system is presented. The diagnosis system is based on state estimators, namely dynamic observers or Kalman filters designed in deterministic and stochastic environment, respectively, and uses residual analysis and statistical tests for fault detection and isolation. The state estimators are obtained from input-output data process and standard identification techniques based on ARX or errors-in-variables models, depending on signal to noise ratio. In the latter case the Kalman filter parameters, i.e., the model parameters and input-output noise variances, are obtained by processing the noisy data according to the Frisch scheme rules. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model. Results from simulation show that minimum detectable faults are perfectly compatible with the industrial target of this application.

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