Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Reliable Detection of a Variance Increase in a Critical Process Variable Linköping University Electronic Press Conference Proceedings
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Title:
Reliable Detection of a Variance Increase in a Critical Process Variable
Author:
Mika Pylvänäinen: Mechatronics and Machine Diagnostics Research Unit, University of Oulu, Finland Toni Liedes: Mechatronics and Machine Diagnostics Research Unit, University of Oulu, Finland
DOI:
10.3384/ecp17142605
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
088
Pages:
605-611
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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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

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

Author:
Mika Pylvänäinen, Toni Liedes
Title:
Reliable Detection of a Variance Increase in a Critical Process Variable
DOI:
http://dx.doi.org/10.3384/ecp17142605
References:
No references available

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

Author:
Mika Pylvänäinen, Toni Liedes
Title:
Reliable Detection of a Variance Increase in a Critical Process Variable
DOI:
https://doi.org10.3384/ecp17142605
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


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Last updated: 2019-10-02