Episode rule-based prognosis applied to complex vacuum pumping systems using vibratory data

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. © 2010 Springer-Verlag Berlin Heidelberg.