Modeling sensor networks for predictive maintenance

Jan Zenisek; Josef Wolfartsberger; Christoph Sievi; Michael Affenzeller

doi:10.1007/978-3-030-11683-5_20

Modeling sensor networks for predictive maintenance

Jan Zenisek, Josef Wolfartsberger, Christoph Sievi, Michael Affenzeller

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

2 Zitate (Scopus)

Abstract

Predictive Maintenance is one of the most intensively investigated topics in the current Industry 4.0 movement. It aims at scheduling maintenance actions based on industrial production plants’ past and current condition and therefore incorporates other trending technological developments such as the Internet of Things, Cyber-Physical Systems or Big Data Analytics. In this short paper we motivate the employment of machine learning algorithms to detect changing behavior as indication for the necessity of maintenance on a microscopic level and describe how cyber-physical environments benefit from this approach.

Originalsprache	Englisch
Titel	On the Move to Meaningful Internet Systems
Untertitel	OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers
Redakteure/-innen	Peter Bollen, Ioana Ciuciu, Robert Meersman, Hervé Panetto, Wided Guédria, Christophe Debruyne
Herausgeber (Verlag)	Springer
Seiten	184-188
Seitenumfang	5
ISBN (Print)	9783030116828
DOIs	https://doi.org/10.1007/978-3-030-11683-5_20
Publikationsstatus	Veröffentlicht - 2019
Veranstaltung	Confederated International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), OTM 2018 - Valletta, Malta Dauer: 22 Okt. 2018 → 26 Okt. 2018

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	11231 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	Confederated International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), OTM 2018
Land/Gebiet	Malta
Ort	Valletta
Zeitraum	22.10.2018 → 26.10.2018

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10.1007/978-3-030-11683-5_20

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Zenisek, J., Wolfartsberger, J., Sievi, C., & Affenzeller, M. (2019). Modeling sensor networks for predictive maintenance. in P. Bollen, I. Ciuciu, R. Meersman, H. Panetto, W. Guédria, & C. Debruyne (Hrsg.), On the Move to Meaningful Internet Systems: OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers (S. 184-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11231 LNCS). Springer. https://doi.org/10.1007/978-3-030-11683-5_20

Zenisek, Jan ; Wolfartsberger, Josef ; Sievi, Christoph et al. / Modeling sensor networks for predictive maintenance. On the Move to Meaningful Internet Systems: OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers. Hrsg. / Peter Bollen ; Ioana Ciuciu ; Robert Meersman ; Hervé Panetto ; Wided Guédria ; Christophe Debruyne. Springer, 2019. S. 184-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Modeling sensor networks for predictive maintenance",

abstract = "Predictive Maintenance is one of the most intensively investigated topics in the current Industry 4.0 movement. It aims at scheduling maintenance actions based on industrial production plants{\textquoteright} past and current condition and therefore incorporates other trending technological developments such as the Internet of Things, Cyber-Physical Systems or Big Data Analytics. In this short paper we motivate the employment of machine learning algorithms to detect changing behavior as indication for the necessity of maintenance on a microscopic level and describe how cyber-physical environments benefit from this approach.",

keywords = "Digital twin, Machine learning, Predictive maintenance",

author = "Jan Zenisek and Josef Wolfartsberger and Christoph Sievi and Michael Affenzeller",

note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Confederated International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), OTM 2018 ; Conference date: 22-10-2018 Through 26-10-2018",

year = "2019",

doi = "10.1007/978-3-030-11683-5_20",

language = "English",

isbn = "9783030116828",

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

publisher = "Springer",

pages = "184--188",

editor = "Peter Bollen and Ioana Ciuciu and Robert Meersman and Herv{\'e} Panetto and Wided Gu{\'e}dria and Christophe Debruyne",

booktitle = "On the Move to Meaningful Internet Systems",

address = "Germany",

}

Zenisek, J , Wolfartsberger, J, Sievi, C & Affenzeller, M 2019, Modeling sensor networks for predictive maintenance. in P Bollen, I Ciuciu, R Meersman, H Panetto, W Guédria & C Debruyne (Hrsg.), On the Move to Meaningful Internet Systems: OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 11231 LNCS, Springer, S. 184-188, Confederated International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), OTM 2018, Valletta, Malta, 22.10.2018. https://doi.org/10.1007/978-3-030-11683-5_20

Modeling sensor networks for predictive maintenance. / Zenisek, Jan ; Wolfartsberger, Josef; Sievi, Christoph et al.
On the Move to Meaningful Internet Systems: OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers. Hrsg. / Peter Bollen; Ioana Ciuciu; Robert Meersman; Hervé Panetto; Wided Guédria; Christophe Debruyne. Springer, 2019. S. 184-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11231 LNCS).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Modeling sensor networks for predictive maintenance

AU - Zenisek, Jan

AU - Wolfartsberger, Josef

AU - Sievi, Christoph

AU - Affenzeller, Michael

PY - 2019

Y1 - 2019

N2 - Predictive Maintenance is one of the most intensively investigated topics in the current Industry 4.0 movement. It aims at scheduling maintenance actions based on industrial production plants’ past and current condition and therefore incorporates other trending technological developments such as the Internet of Things, Cyber-Physical Systems or Big Data Analytics. In this short paper we motivate the employment of machine learning algorithms to detect changing behavior as indication for the necessity of maintenance on a microscopic level and describe how cyber-physical environments benefit from this approach.

AB - Predictive Maintenance is one of the most intensively investigated topics in the current Industry 4.0 movement. It aims at scheduling maintenance actions based on industrial production plants’ past and current condition and therefore incorporates other trending technological developments such as the Internet of Things, Cyber-Physical Systems or Big Data Analytics. In this short paper we motivate the employment of machine learning algorithms to detect changing behavior as indication for the necessity of maintenance on a microscopic level and describe how cyber-physical environments benefit from this approach.

KW - Digital twin

KW - Machine learning

KW - Predictive maintenance

UR - http://www.scopus.com/inward/record.url?scp=85062289564&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-11683-5_20

DO - 10.1007/978-3-030-11683-5_20

M3 - Conference contribution

SN - 9783030116828

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

SP - 184

EP - 188

BT - On the Move to Meaningful Internet Systems

A2 - Bollen, Peter

A2 - Ciuciu, Ioana

A2 - Meersman, Robert

A2 - Panetto, Hervé

A2 - Guédria, Wided

A2 - Debruyne, Christophe

PB - Springer

T2 - Confederated International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), OTM 2018

Y2 - 22 October 2018 through 26 October 2018

ER -

Zenisek J , Wolfartsberger J, Sievi C, Affenzeller M. Modeling sensor networks for predictive maintenance. in Bollen P, Ciuciu I, Meersman R, Panetto H, Guédria W, Debruyne C, Hrsg., On the Move to Meaningful Internet Systems: OTM 2018 Workshops - Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Revised Selected Papers. Springer. 2019. S. 184-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11683-5_20

Modeling sensor networks for predictive maintenance

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