hh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
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 (Engelska)Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 53, s. 447-456Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Amsterdam: Elsevier, 2015. Vol. 53, s. 447-456
Nyckelord [en]
Vehicle diagnostics, predictive maintenance, fault detection, self-organizing systems
Nationell ämneskategori
Signalbehandling Systemvetenskap, informationssystem och informatik
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
Konferens
INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015
Projekt
In4Uptime
Forskningsfinansiär
VINNOVATillgänglig från: 2015-08-19 Skapad: 2015-08-19 Senast uppdaterad: 2022-02-10Bibliografiskt 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
2. Wisdom of the Crowd for Fault Detection and Prognosis
Öppna denna publikation i ny flik eller fönster >>Wisdom of the Crowd for Fault Detection and Prognosis
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. 

This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers. 

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2020. s. 87
Serie
Halmstad University Dissertations ; 67
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
urn:nbn:se:hh:diva-41367 (URN)978-91-88749-43-7 (ISBN)978-91-88749-42-0 (ISBN)
Disputation
2020-01-31, J102 Wigforss, Kristian IV:s väg 3, Halmstad, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-01-14 Skapad: 2020-01-10 Senast uppdaterad: 2021-01-12Bibliografiskt granskad

Open Access i DiVA

fulltext(5248 kB)663 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 5248 kBChecksumma SHA-512
1a3a9cb58b84ab1f9f09e2b1ab879f1207b2bdab0b10371986c10a6e719f3b87cb668e88fb0a7e10d2d1021a1e7bbc43476e2a83748b0811454885cc456571d0
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextScopus

Person

Fan, YuantaoNowaczyk, SławomirRögnvaldsson, Thorsteinn

Sök vidare i DiVA

Av författaren/redaktören
Fan, YuantaoNowaczyk, SławomirRögnvaldsson, Thorsteinn
Av organisationen
CAISR Centrum för tillämpade intelligenta system (IS-lab)Laboratoriet för intelligenta system
I samma tidskrift
Procedia Computer Science
SignalbehandlingSystemvetenskap, informationssystem och informatik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 663 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 936 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf