Abstract
This paper presents a local pattern-based method that addresses system prognosis. It also details a successful application to complex vacuum pumping systems. More precisely, using historical vibratory data, we first model the behavior of systems 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 vibratory 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.
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Martin, F., Méger, N., Galichet, S., Becourt, N. (2010). Episode Rule-Based Prognosis Applied to Complex Vacuum Pumping Systems Using Vibratory Data. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_29
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DOI: https://doi.org/10.1007/978-3-642-14400-4_29
Publisher Name: Springer, Berlin, Heidelberg
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