Data-driven prognosis applied to complex vacuum pumping systems

Authors:
Florent Martin;Nicolas Meger;Sylvie Galichet;Nicolas Becourt
Affiliations:
University of Savoie, Polytech'Savoie, LISTIC, Annecy-le-Vieux, France and Alcatel Vacuum Technology, Annecy, France;University of Savoie, Polytech'Savoie, LISTIC, Annecy-le-Vieux, France;University of Savoie, Polytech'Savoie, LISTIC, Annecy-le-Vieux, France;Alcatel Vacuum Technology, Annecy, France
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Year:
2010

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Abstract

This paper presents a method to address system prognosis. It also details a successful application to complex vacuum pumping systems. The proposed approach relies on an automated data-driven learning process as opposed to hand-built models that are based on human expertise. More precisely, using historical vibratory data, we first model the behavior of a system by extracting a given type of episode rules, namely First Local Maximum episode rules (FLM-rules). A subset of the extracted FLM-rules is then selected in order to further predict pumping system failures in a datastream context. The results that we got for production data are very encouraging as we predict failures with a good time scale precision. We are now deploying our solution for a customer of the semi-conductor market.