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Self-monitoring for maintenance of vehicle fleets
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0001-5163-2997
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
Volvo Group Trucks Technology, Göteborg, Sweden.ORCID-id: 0000-0001-8255-1276
Vise andre og tillknytning
2018 (engelsk)Inngår i: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, nr 2, s. 344-384Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g. a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost four years. © 2017 The Author(s)

sted, utgiver, år, opplag, sider
New York: Springer-Verlag New York, 2018. Vol. 32, nr 2, s. 344-384
Emneord [en]
Data Mining, Knowledge Discovery, Empirical Studies, Vehicle Fleet Maintenance
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Identifikatorer
URN: urn:nbn:se:hh:diva-34746DOI: 10.1007/s10618-017-0538-6Scopus ID: 2-s2.0-85027693423OAI: oai:DiVA.org:hh-34746DiVA, id: diva2:1134103
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VINNOVAKnowledge FoundationTilgjengelig fra: 2017-08-17 Laget: 2017-08-17 Sist oppdatert: 2018-02-27bibliografisk kontrollert

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Rögnvaldsson, ThorsteinnNowaczyk, SławomirByttner, StefanPrytz, Rune
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