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Episode Rule-Based Prognosis Applied to Complex Vacuum Pumping Systems Using Vibratory Data

  • Florent Martin
  • Nicolas Méger
  • Sylvie Galichet
  • Nicolas Becourt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)

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.

Keywords

episode rules FLM-rules predictive maintenance prognosis vibratory signals 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Florent Martin
    • 1
    • 2
  • Nicolas Méger
    • 1
  • Sylvie Galichet
    • 1
  • Nicolas Becourt
    • 2
  1. 1.University of Savoie, Polytech’Savoie, LISTIC laboratoryAnnecy-le-VieuxFrance
  2. 2.Alcatel Vacuum technologyAnnecyFrance

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