hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Domain Adaptation in Predicting Turbocharger Failures Using Vehicle's Sensor Measurements
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-2590-6661
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-0051-0954
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology.ORCID iD: 0000-0001-5163-2997
Show others and affiliations
2022 (English)In: PHM Society European Conference / [ed] Phuc Do; Gabriel Michau; Cordelia Ezhilarasu, State College, PA: PHM Society , 2022, Vol. 7 (1), p. 432-439Conference paper, Published paper (Refereed)
Abstract [en]

The discrepancy in the distribution of source and target domains is usually referred to as a domain shift. It is one of the reasons for the inferior performance of machine learning solutions at deployment. We illustrate that the domain shift issue is pertinent to the readings of the vehicles’ operational sensors. This is due to the fact that these measurements are collected over a period of time and are susceptible to various changes that happen in the meantime. Examples of these changes are usage pattern variations, aging of the vehicles, seasonal shifts, and driver changes. However, domain adversarial neural networks (DANN) have shown promising results to reduce the negative impact of the domain shift. The present study investigates domain adaptation (DA) in the predictive maintenance field by estimating the remaining useful life (RUL) of turbochargers. The devices are operating on a fleet of VOLVO trucks, and the information about their services is collected over four years between 2016 and 2019. The input features to the model are a set of bi-weekly collected measurements called logged vehicle data (LVD). The contributions of this paper are two-fold. First, we propose a new approach for detecting domain (covariate) shift using an autoencoder. Second, we adapt domain adversarial neural networks to the specific application of predicting turbocharger failures. Finally, we deploy a recurrent feature extraction layer in the DANN architecture to incorporate temporal aspect of the data. The experimental results demonstrate the superiority of the proposed method over the traditional approach.

Place, publisher, year, edition, pages
State College, PA: PHM Society , 2022. Vol. 7 (1), p. 432-439
Series
Proceedings of the European Conference of the Prognostics and Health Management Society (PHME), E-ISSN 2325-016X
Keywords [en]
Remaining useful life, Prognostics, turbocharger, Domain adversarial neural networks
National Category
Computer Sciences Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-49147DOI: 10.36001/phme.2022.v7i1.3340ISBN: 978-1-936263-36-3 (electronic)OAI: oai:DiVA.org:hh-49147DiVA, id: diva2:1725097
Conference
7th European Conference of the Prognostics and Health Management (PHM) Society, Turin, Italy, 6-8 July, 2022
Funder
Knowledge FoundationVinnovaAvailable from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-03-21Bibliographically approved

Open Access in DiVA

fulltext(1093 kB)50 downloads
File information
File name FULLTEXT01.pdfFile size 1093 kBChecksum SHA-512
9a1ba88dededa5e381dd0d8acd80a569c4ad614d78870b744f3f9ab569849c76765e7cf3d190ce221e3553452aeaec63916c30a0b6d50ac610e3fd0dcbf8725b
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Rahat, MahmoudSheikholharam Mashhadi, PeymanNowaczyk, SławomirRögnvaldsson, Thorsteinn

Search in DiVA

By author/editor
Rahat, MahmoudSheikholharam Mashhadi, PeymanNowaczyk, SławomirRögnvaldsson, Thorsteinn
By organisation
Center for Applied Intelligent Systems Research (CAISR)School of Information Technology
Computer SciencesComputer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 50 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 144 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf