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Towards improved analysis methods for two-level factorial experiments with time series responses
Luleå University of Technology, Quality Technology and Management.
Luleå University of Technology, Quality Technology and Management.
University West, Department of Engineering Science, Division of Natural Sciences and Electrical and Surveying Engineering. (PTW)
2013 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 5, p. 725-741Article in journal (Refereed) Published
Abstract [en]

Dynamic processes exhibit a time delay between the disturbances and the resulting process response. Therefore, one has to acknowledge process dynamics, such as transition times, when planning and analyzing experiments in dynamic processes. In this article, we explore, discuss, and compare different methods to estimate location effects for two-level factorial experiments where the responses are represented by time series. Particularly, we outline the use of intervention-noise modeling to estimate the effects and to compare this method by using the averages of the response observations in each run as the single response. The comparisons are made by simulated experiments using a dynamic continuous process model. The results show that the effect estimates for the different analysis methods are similar. Using the average of the response in each run, but removing the transition time, is found to be a competitive, robust, and straightforward method, whereas intervention-noise models are found to be more comprehensive, render slightly fewer spurious effects, find more of the active effects for unreplicated experiments and provide the possibility to model effect dynamics. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
2013. Vol. 29, no 5, p. 725-741
Keywords [en]
intervention-noise model, location effects, simulation, time series analysis, two-level factorial design, Work-integrated learning, WIL
Keywords [sv]
AIL
National Category
Computational Mathematics
Research subject
ENGINEERING, Mathematics; Work Integrated Learning
Identifiers
URN: urn:nbn:se:hv:diva-5573DOI: 10.1002/qre.1424ISI: 000322222600012Scopus ID: 2-s2.0-84880841527OAI: oai:DiVA.org:hv-5573DiVA, id: diva2:641753
Available from: 2013-08-19 Created: 2013-08-13 Last updated: 2019-06-03Bibliographically approved

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Vännman, Kerstin

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