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In this paper, we propose a supervision method which aims at determining pertinent indicators to optimize predictive maintenance strategies. The supervision method, based on the AUto-adaptative and Dynamical Clustering technique (AUDyC), consists in classifying in real time measured data into classes representative of the operating modes of the process. This technique also allows the detection and the tracking of the slow evolutions of the process modes. Based on the AUDyC technique, a method is proposed to estimate the probabilities of the failure occurence of components in real time. This method is illustrated on the real case of a temperature controller.

DYNAMICAL CLUSTERING TECHNIQUE TO ESTIMATE THE PROBABILITY OF THE FAILURE OCCURRENCE OF PROCESS SUBJECTED TO SLOW DEGRADATION