University of Oulu

Juuso, Esko K. (2018) Recursive data analysis in large scale complex systems. In: Linköping Electronic Conference Proceedings vol. 142, Proceedings of the 9th EUROSIM Congress on Modelling and Simulation, 12-16 September 2016 in Oulu, Finland, (pp.1053-1059). http://dx.doi.org/10.3384/ecp171421053

Recursive data analysis in large scale complex systems

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Author: Juuso, Esko K.1
Organizations: 1Control Engineering, Faculty of Technology, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022022120224
Language: English
Published: Linköping University Electronic Press, 2018
Publish Date: 2022-02-21
Description:

Abstract

Advanced data analysis is needed in practical applications in large scale complex systems. Variable specific datadriven solutions provide consistent levels, which can be used in compact model structures. In changing operating conditions, the recursive analysis extends the applicability of these structures in building and tuning dynamic and case-based models for complex systems since the meanings change more frequently than the interactions. The methodology provides information about uncertainty, fluctuations and confidence in results. The scaling approach brings temporal analysis to all measurements and features: trend indices are calculated by comparing the averages in the long and short time windows, a weighted sum of the trend index and its derivative detects the trend episodes and severity of the trend is estimated by including also the variable level in the sum. The trend episodes and temporal adaptation of the scaling functions with time are used in the early detection of changes in the operating conditions. The levels are understood as fuzzy labels and the decision making is based on fuzzy calculus. The solution is highly compact: all variables, features and indices are transformed to the range [-2, 2] and represented in natural language which is important in integrating datadriven solutions with domain expertise.

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Series: Linköping electronic conference proceedings
ISSN: 1650-3686
ISSN-E: 1650-3740
ISSN-L: 1650-3686
ISBN: 978-91-7685-399-3
Volume: 142
Pages: 1053 - 1059
DOI: 10.3384/ecp171421053
OADOI: https://oadoi.org/10.3384/ecp171421053
Host publication: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Host publication editor: Juuso, Esko
Dahlquist, Erik
Leiviskä, Kauko
Conference: EUROSIM
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
114 Physical sciences
213 Electronic, automation and communications engineering, electronics
222 Other engineering and technologies
Subjects:
Funding: The author would like to thank the research program Measurement, Monitoring and Environmental Efficiency Assesment (MMEA) funded by the TEKES (the Finnish Funding Agency for Technology and Innovation).
Copyright information: © Scandinavian Simulation Society, 2018.