EARLY WARNING OF SLIGHT CHANGES IN SYSTEMS

Techniques for early warning of slight changes in systems and plants are useful for condition-based maintenance. In this paper we present an approach for this problem. This approach is based on the so-called 'asymptotic local' approach for change detection previously introduced by some of the authors. Its original principle consists in characterizing a system via some identified model, and then to monitor its changes using some data-to-model distance also derived from identification techniques. We show here that this method is of much wider applicability: model reduction can be enforced, biased identification procedures can be used, and finally one can even get rid of identification and use instead some much simpler Monte-Carlo estimation technique prior to change detection. Experiments on AR models are reported and an example from gas turbine industry is briefly discussed.