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Self-monitoring for maintenance of vehicle fleets
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5163-2997
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.
Volvo Group Trucks Technology, Göteborg, Sweden.ORCID iD: 0000-0001-8255-1276
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2018 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, no 2, p. 344-384Article in journal (Refereed) 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)

Place, publisher, year, edition, pages
New York: Springer-Verlag New York, 2018. Vol. 32, no 2, p. 344-384
Keyword [en]
Data Mining, Knowledge Discovery, Empirical Studies, Vehicle Fleet Maintenance
National Category
Embedded Systems
Identifiers
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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ReDi2ServiceCAISR
Funder
VINNOVAKnowledge Foundation
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-02-27Bibliographically approved

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Rögnvaldsson, ThorsteinnNowaczyk, SławomirByttner, StefanPrytz, Rune

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