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Consensus self-organized models for fault detection (COSMO)
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).ORCID-id: 0000-0001-5163-2997
Volvo Technology, SE-405 08 Göteborg, Sweden.
2011 (Engelska)Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 24, nr 5, s. 833-839Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field-bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles’ data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.

Ort, förlag, år, upplaga, sidor
Oxford: Pergamon Press, 2011. Vol. 24, nr 5, s. 833-839
Nyckelord [en]
Fault detection, Fleet management, Remote maintenance, Self-organizing systems, Telematics
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:hh:diva-15085DOI: 10.1016/j.engappai.2011.03.002ISI: 000291524200010Scopus ID: 2-s2.0-79956149234OAI: oai:DiVA.org:hh-15085DiVA, id: diva2:416942
Tillgänglig från: 2011-05-13 Skapad: 2011-05-13 Senast uppdaterad: 2018-03-23Bibliografiskt granskad

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Byttner, StefanRögnvaldsson, ThorsteinnSvensson, Magnus

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