Process dynamics study based on multivariate AR-modelling

Seppo Rantala; Heikki Jokinen; Martti Jokipii; Olli Saarela; Risto Suoranta

doi:10.1109/IECON.1988.665158

Process dynamics study based on multivariate AR-modelling

Seppo Rantala^*, Heikki Jokinen, Martti Jokipii, Olli Saarela, Risto Suoranta

^*Corresponding author for this work

Tampere University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

Abstract

In this paper a method for studying process dynamics will be presented. The method is based on statistical time series modelling called multivariate autoregressive modelling. The proposed method can be used for trouble-shooting diagnostics and control system modelling applications. After the multivariate autoregressive model is identified the dynamics of the system can be analyzed with the model. The model parameters are used for determination of power spectrum estimates, transfer functions with step responses and noise source contributions. Those parameters can be used also for simulation purposes. The result of the present study suggests effectiveness and usefulness of the method as a tool in process dynamics research.

Original language	English
Title of host publication	Proceedings of the 14th Annual Conference of Industrial Electronics Society
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	320-325
Number of pages	6
DOIs	https://doi.org/10.1109/IECON.1988.665158
Publication status	Published - 1988
MoE publication type	A4 Article in a conference publication
Event	1988 International Conference on Industrial Electronics: Control and Simulation, IECON 1988 - Singapore, Singapore Duration: 24 Oct 1988 → 28 Oct 1988

Conference

Conference	1988 International Conference on Industrial Electronics: Control and Simulation, IECON 1988
Country/Territory	Singapore
City	Singapore
Period	24/10/88 → 28/10/88

Keywords

Autoregressive modelling
Identification
Multivariable systems
Process dynamics
Signal and system modelling
Signal processing
Simulation
System analysis
Technical diagnostics
Time series analysis

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10.1109/IECON.1988.665158

Cite this

@inproceedings{b4a147848f3c4d69bb32ea32d6d7e49f,

title = "Process dynamics study based on multivariate AR-modelling",

abstract = "In this paper a method for studying process dynamics will be presented. The method is based on statistical time series modelling called multivariate autoregressive modelling. The proposed method can be used for trouble-shooting diagnostics and control system modelling applications. After the multivariate autoregressive model is identified the dynamics of the system can be analyzed with the model. The model parameters are used for determination of power spectrum estimates, transfer functions with step responses and noise source contributions. Those parameters can be used also for simulation purposes. The result of the present study suggests effectiveness and usefulness of the method as a tool in process dynamics research.",

keywords = "Autoregressive modelling, Identification, Multivariable systems, Process dynamics, Signal and system modelling, Signal processing, Simulation, System analysis, Technical diagnostics, Time series analysis",

author = "Seppo Rantala and Heikki Jokinen and Martti Jokipii and Olli Saarela and Risto Suoranta",

year = "1988",

doi = "10.1109/IECON.1988.665158",

language = "English",

pages = "320--325",

booktitle = "Proceedings of the 14th Annual Conference of Industrial Electronics Society",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "1988 International Conference on Industrial Electronics: Control and Simulation, IECON 1988 ; Conference date: 24-10-1988 Through 28-10-1988",

}

Rantala, S, Jokinen, H, Jokipii, M, Saarela, O & Suoranta, R 1988, Process dynamics study based on multivariate AR-modelling. in Proceedings of the 14th Annual Conference of Industrial Electronics Society. IEEE Institute of Electrical and Electronic Engineers, pp. 320-325, 1988 International Conference on Industrial Electronics: Control and Simulation, IECON 1988, Singapore, Singapore, 24/10/88. https://doi.org/10.1109/IECON.1988.665158

Process dynamics study based on multivariate AR-modelling. / Rantala, Seppo; Jokinen, Heikki; Jokipii, Martti et al.
Proceedings of the 14th Annual Conference of Industrial Electronics Society. IEEE Institute of Electrical and Electronic Engineers, 1988. p. 320-325.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Process dynamics study based on multivariate AR-modelling

AU - Rantala, Seppo

AU - Jokinen, Heikki

AU - Jokipii, Martti

AU - Saarela, Olli

AU - Suoranta, Risto

PY - 1988

Y1 - 1988

N2 - In this paper a method for studying process dynamics will be presented. The method is based on statistical time series modelling called multivariate autoregressive modelling. The proposed method can be used for trouble-shooting diagnostics and control system modelling applications. After the multivariate autoregressive model is identified the dynamics of the system can be analyzed with the model. The model parameters are used for determination of power spectrum estimates, transfer functions with step responses and noise source contributions. Those parameters can be used also for simulation purposes. The result of the present study suggests effectiveness and usefulness of the method as a tool in process dynamics research.

AB - In this paper a method for studying process dynamics will be presented. The method is based on statistical time series modelling called multivariate autoregressive modelling. The proposed method can be used for trouble-shooting diagnostics and control system modelling applications. After the multivariate autoregressive model is identified the dynamics of the system can be analyzed with the model. The model parameters are used for determination of power spectrum estimates, transfer functions with step responses and noise source contributions. Those parameters can be used also for simulation purposes. The result of the present study suggests effectiveness and usefulness of the method as a tool in process dynamics research.

KW - Autoregressive modelling

KW - Identification

KW - Multivariable systems

KW - Process dynamics

KW - Signal and system modelling

KW - Signal processing

KW - Simulation

KW - System analysis

KW - Technical diagnostics

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=77955558508&partnerID=8YFLogxK

U2 - 10.1109/IECON.1988.665158

DO - 10.1109/IECON.1988.665158

M3 - Conference article in proceedings

AN - SCOPUS:77955558508

SP - 320

EP - 325

BT - Proceedings of the 14th Annual Conference of Industrial Electronics Society

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 1988 International Conference on Industrial Electronics: Control and Simulation, IECON 1988

Y2 - 24 October 1988 through 28 October 1988

ER -

Process dynamics study based on multivariate AR-modelling

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