Fault propagation analysis by combining data-driven causal analysis and plant connectivity

Rinat Landman; Jukka Kortela; Sirkka Liisa Jämsä-Jounela

doi:10.1109/ETFA.2014.7005337

Fault propagation analysis by combining data-driven causal analysis and plant connectivity

Rinat Landman, Jukka Kortela, Sirkka Liisa Jämsä-Jounela

Department of Chemical and Metallurgical Engineering

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

3 Citations (Scopus)

Abstract

This paper presents a novel technique for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity extracted from a piping and instrumentation diagram. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.

Original language	English
Title of host publication	Emerging Technologies and Factory Automation (ETFA) 2014
Publisher	IEEE
ISBN (Print)	9781479948468
DOIs	https://doi.org/10.1109/ETFA.2014.7005337
Publication status	Published - 8 Jan 2014
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Emerging Technologies and Factory Automation - Barcelona, Spain Duration: 16 Sep 2014 → 19 Sep 2014 Conference number: 19

Conference

Conference	IEEE International Conference on Emerging Technologies and Factory Automation
Abbreviated title	ETFA
Country/Territory	Spain
City	Barcelona
Period	16/09/2014 → 19/09/2014

Keywords

causal analysis
control loops
Plant topology

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10.1109/ETFA.2014.7005337

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title = "Fault propagation analysis by combining data-driven causal analysis and plant connectivity",

abstract = "This paper presents a novel technique for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity extracted from a piping and instrumentation diagram. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.",

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Landman, R, Kortela, J & Jämsä-Jounela, SL 2014, Fault propagation analysis by combining data-driven causal analysis and plant connectivity. in Emerging Technologies and Factory Automation (ETFA) 2014., 7005337, IEEE, IEEE International Conference on Emerging Technologies and Factory Automation, Barcelona, Spain, 16/09/2014. https://doi.org/10.1109/ETFA.2014.7005337

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AU - Landman, Rinat

AU - Kortela, Jukka

AU - Jämsä-Jounela, Sirkka Liisa

N1 - Conference code: 19

PY - 2014/1/8

Y1 - 2014/1/8

N2 - This paper presents a novel technique for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity extracted from a piping and instrumentation diagram. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.

AB - This paper presents a novel technique for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity extracted from a piping and instrumentation diagram. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.

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KW - control loops

KW - Plant topology

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M3 - Conference contribution

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Y2 - 16 September 2014 through 19 September 2014

ER -

Fault propagation analysis by combining data-driven causal analysis and plant connectivity

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Keywords

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