Conference article

Reliable Detection of a Variance Increase in a Critical Process Variable

Mika Pylvänäinen
Mechatronics and Machine Diagnostics Research Unit, University of Oulu, Finland

Toni Liedes
Mechatronics and Machine Diagnostics Research Unit, University of Oulu, Finland

Download articlehttp://dx.doi.org/10.3384/ecp17142605

Published in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Linköping Electronic Conference Proceedings 142:88, p. 605-611

Show more +

Published: 2018-12-19

ISBN: 978-91-7685-399-3

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

Industrial process failures can be often seen as a variance increase in a measured process variable. The objective of this research was to investigate if stochastic Autoregressive Moving Average, abbreviated as ARMA, and Generalized Autoregressive Conditionally Heteroscedastic, abbreviated as GARCH, time series modelling are feasible methods for the reliable detection of gradually increasing variance in the process variable. A case study was conducted for the reliable detection of increased pressure variance that indicates a harmful air leakage in a vacuum chamber in a paper machine. Variance in the chamber pressure was artificially gradually increased, a combined ARMA+GARCH time series model was fitted to it and the variance vector was determined. An abnormally high variance was detected from the variance vector using a specified detection limit and detection sensitivity. According to the simulation results, by controlling the variance vector extracted from the combined ARMA+GARCH time series model, a very slight variance increase in the process variable can be detected more reliably than detecting it from the moving variance vector computed directly from the process variable.

Keywords

condition monitoring, time series analysis, SPC

References

No references available

Citations in Crossref