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Monitoring equipment operation through model and event discovery
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-6249-4144
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3034-6630
2018 (English)In: Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / [ed] Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros, Cham: Springer, 2018, Vol. 11315, p. 41-53Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.

Place, publisher, year, edition, pages
Cham: Springer, 2018. Vol. 11315, p. 41-53
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11315
Keywords [en]
Artificial intelligence, Computer science, Computers, Event discoveries, Human expert, Human supervision, Monitoring approach, Monitoring equipment, Multiple variants, Real time, Scaling-up, Real time systems
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:hh:diva-38732DOI: 10.1007/978-3-030-03496-2_6Scopus ID: 2-s2.0-85057087564ISBN: 9783030034955 (print)ISBN: 978-3-030-03496-2 (electronic)OAI: oai:DiVA.org:hh-38732DiVA, id: diva2:1276685
Conference
Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved

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Nowaczyk, SławomirPinheiro Sant'Anna, AnitaCalikus, EceFan, Yuantao

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