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.
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Martin, F., Meger, N., Galichet, S., Becourt, N. (2010). Data-Driven Prognosis Applied to Complex Vacuum Pumping Systems. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_47
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DOI: https://doi.org/10.1007/978-3-642-13022-9_47
Publisher Name: Springer, Berlin, Heidelberg
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