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Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3034-6630
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.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).ORCID-id: 0000-0001-5163-2997
2015 (engelsk)Inngår i: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, s. 447-456Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.

sted, utgiver, år, opplag, sider
Amsterdam: Elsevier, 2015. Vol. 53, s. 447-456
Emneord [en]
Vehicle diagnostics, predictive maintenance, fault detection, self-organizing systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-29240DOI: 10.1016/j.procs.2015.07.322ISI: 000360311000051Scopus ID: 2-s2.0-84939156791OAI: oai:DiVA.org:hh-29240DiVA, id: diva2:847249
Konferanse
INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015
Prosjekter
In4Uptime
Forskningsfinansiär
VINNOVATilgjengelig fra: 2015-08-19 Laget: 2015-08-19 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Inngår i avhandling
1. A Self-Organized Fault Detection Method for Vehicle Fleets
Åpne denne publikasjonen i ny fane eller vindu >>A Self-Organized Fault Detection Method for Vehicle Fleets
2016 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

A fleet of commercial heavy-duty vehicles is a very interesting application arena for fault detection and predictive maintenance. With a highly digitized electronic system and hundreds of sensors mounted on-board a modern bus, a huge amount of data is generated from daily operations.

This thesis and appended papers present a study of an autonomous framework for fault detection, using the data gathered from the regular operation of vehicles. We employed an unsupervised deviation detection method, called Consensus Self-Organising Models (COSMO), which is based on the concept of ‘wisdom of the crowd’. It assumes that the majority of the group is ‘healthy’; by comparing individual units within the group, deviations from the majority can be considered as potentially ‘faulty’. Information regarding detected anomalies can be utilized to prevent unplanned stops.

This thesis demonstrates how knowledge useful for detecting faults and predicting failures can be autonomously generated based on the COSMO method, using different generic data representations. The case study in this work focuses on vehicle air system problems of a commercial fleet of city buses. We propose an approach to evaluate the COSMO method and show that it is capable of detecting various faults and indicates upcoming air compressor failures. A comparison of the proposed method with an expert knowledge based system shows that both methods perform equally well. The thesis also analyses the usage and potential benefits of using the Echo State Network as a generic data representation for the COSMO method and demonstrates the capability of Echo State Network to capture interesting characteristics in detecting different types of faults.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2016. s. 116
Serie
Halmstad University Dissertations ; 27
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-32489 (URN)978-91-87045-57-8 (ISBN)978-91-87045-56-1 (ISBN)
Presentation
2016-12-16, Halda, Kristian IV:s väg 3, 301 18 Halmstad, Halmstad, 10:00 (engelsk)
Opponent
Veileder
Prosjekter
In4Uptime
Forskningsfinansiär
VINNOVA
Tilgjengelig fra: 2016-11-28 Laget: 2016-11-25 Sist oppdatert: 2016-11-28bibliografisk kontrollert

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