Sliding window symbolic regression for predictive maintenance using model ensembles

Jan Zenisek; Michael Affenzeller; Josef Wolfartsberger; Mathias Silmbroth; Christoph Sievi; Aziz Huskic; Herbert Jodlbauer

doi:10.1007/978-3-319-74718-7_58

Sliding window symbolic regression for predictive maintenance using model ensembles

Jan Zenisek, Michael Affenzeller, Josef Wolfartsberger, Mathias Silmbroth, Christoph Sievi, Aziz Huskic, Herbert Jodlbauer

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

3 Citations (Scopus)

Abstract

Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyze a continuous stream of data, which describes a system’s condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	481-488
Number of pages	8
ISBN (Print)	9783319747170
DOIs	https://doi.org/10.1007/978-3-319-74718-7_58
Publication status	Published - 2018
Event	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain Duration: 19 Feb 2017 → 24 Feb 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10671 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	19.02.2017 → 24.02.2017

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10.1007/978-3-319-74718-7_58

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Zenisek, J., Affenzeller, M., Wolfartsberger, J., Silmbroth, M., Sievi, C., Huskic, A., & Jodlbauer, H. (2018). Sliding window symbolic regression for predictive maintenance using model ensembles. In R. Moreno-Diaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers (pp. 481-488). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS). Springer. https://doi.org/10.1007/978-3-319-74718-7_58

Zenisek, Jan ; Affenzeller, Michael ; Wolfartsberger, Josef et al. / Sliding window symbolic regression for predictive maintenance using model ensembles. Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. editor / Roberto Moreno-Diaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2018. pp. 481-488 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Sliding window symbolic regression for predictive maintenance using model ensembles",

abstract = "Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyze a continuous stream of data, which describes a system{\textquoteright}s condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.",

author = "Jan Zenisek and Michael Affenzeller and Josef Wolfartsberger and Mathias Silmbroth and Christoph Sievi and Aziz Huskic and Herbert Jodlbauer",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 ; Conference date: 19-02-2017 Through 24-02-2017",

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doi = "10.1007/978-3-319-74718-7_58",

language = "English",

isbn = "9783319747170",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Zenisek, J , Affenzeller, M , Wolfartsberger, J, Silmbroth, M, Sievi, C, Huskic, A & Jodlbauer, H 2018, Sliding window symbolic regression for predictive maintenance using model ensembles. in R Moreno-Diaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10671 LNCS, Springer, pp. 481-488, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Las Palmas de Gran Canaria, Spain, 19.02.2017. https://doi.org/10.1007/978-3-319-74718-7_58

Sliding window symbolic regression for predictive maintenance using model ensembles. / Zenisek, Jan ; Affenzeller, Michael ; Wolfartsberger, Josef et al.
Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Diaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2018. p. 481-488 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS).

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

TY - GEN

T1 - Sliding window symbolic regression for predictive maintenance using model ensembles

AU - Zenisek, Jan

AU - Affenzeller, Michael

AU - Wolfartsberger, Josef

AU - Silmbroth, Mathias

AU - Sievi, Christoph

AU - Huskic, Aziz

AU - Jodlbauer, Herbert

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AB - Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyze a continuous stream of data, which describes a system’s condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.

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M3 - Conference contribution

SN - 9783319747170

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 481

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BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers

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A2 - Pichler, Franz

PB - Springer

T2 - 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017

Y2 - 19 February 2017 through 24 February 2017

ER -

Zenisek J , Affenzeller M , Wolfartsberger J, Silmbroth M, Sievi C, Huskic A et al. Sliding window symbolic regression for predictive maintenance using model ensembles. In Moreno-Diaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Springer. 2018. p. 481-488. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-74718-7_58

Sliding window symbolic regression for predictive maintenance using model ensembles

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