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Recurrent Neural Networks for Fault Detection: An exploratory study on a dataset about air compressor failures of heavy duty trucks
Halmstad University, School of Information Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Place, publisher, year, edition, pages
2018.
Keywords [en]
Predictive Maintenance, LSTM, Fault Detection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-38184OAI: oai:DiVA.org:hh-38184DiVA, id: diva2:1257115
Educational program
Master's Programme in Information Technology, 120 credits
Available from: 2018-10-24 Created: 2018-10-18 Last updated: 2018-10-24Bibliographically approved

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fulltext(8684 kB)221 downloads
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File name FULLTEXT02.pdfFile size 8684 kBChecksum SHA-512
3e770517d4f7e54f12a88da1955bee80700678986d26e156c0c570d078b1e5501d48defae1e483a41c4e456ae12796ad47376ed6b005d21f254f1bb07e128775
Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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