Detection of slight changes using reduced models and biased identification

Techniques for early warning of slight changes in systems and plants are useful for condition-based maintenance. An approach to this problem based on the asymptotic local approach to change detection is presented. Its principle consists in characterizing a system via some identified model and then monitoring its changes using some data-to-model distance also derived from identification techniques. It is shown that this method can be used even when only poor identification procedures are available (with bias, with oversimplified models, etc.). An example from the gas turbine industry is discussed.<
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