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Incorporating Expert Knowledge into a 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 (Engelska)Ingår i: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, s. 58-67Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.

In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.

Ort, förlag, år, upplaga, sidor
Amsterdam: IOS Press, 2015. Vol. 278, s. 58-67
Nyckelord [en]
Vehicle diagnostics, Predictive maintenance, Fault detection, Receiver Operating Characteristic curve, Expert knowledge
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:hh:diva-29809DOI: 10.3233/978-1-61499-589-0-58Scopus ID: 2-s2.0-84963636151OAI: oai:DiVA.org:hh-29809DiVA, id: diva2:873690
Konferens
The 13th Scandinavian Conference on Artificial Intelligence (SCAI), Halmstad University, Halmstad, Sweden, 5-6 November, 2015
Projekt
In4Uptime
Forskningsfinansiär
VINNOVAKK-stiftelsen
Anmärkning

ISBN: 978-1-61499-588-3 (print) | 978-1-61499-589-0 (online)

Editor: Sławomir Nowaczyk

Tillgänglig från: 2015-11-24 Skapad: 2015-11-24 Senast uppdaterad: 2018-01-10Bibliografiskt granskad
Ingår i avhandling
1. A Self-Organized Fault Detection Method for Vehicle Fleets
Öppna denna publikation i ny flik eller fönster >>A Self-Organized Fault Detection Method for Vehicle Fleets
2016 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2016. s. 116
Serie
Halmstad University Dissertations ; 27
Nationell ämneskategori
Elektroteknik och elektronik
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 (Engelska)
Opponent
Handledare
Projekt
In4Uptime
Forskningsfinansiär
VINNOVA
Tillgänglig från: 2016-11-28 Skapad: 2016-11-25 Senast uppdaterad: 2016-11-28Bibliografiskt granskad

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Fan, YuantaoNowaczyk, SławomirRögnvaldsson, Thorsteinn

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