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Outlier detection on sparse-encoded vibration signals from rolling element bearings
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The demand for reliable condition monitoring systems on rotating machinery for power generation is continuously increasing due to a wider use of wind power as an energy source, which requires expertise in the diagnostics of these systems. An alternative to the limited availability of diagnostics and maintenance experts in the wind energy sector is to use unsupervised machine learning algorithms as a support tool for condition monitoring. The way condition monitoring systems can employ unsupervised machine learning algorithms consists on prioritizing the assets to monitor via the number of anomalies detected in the vibration signals of the rolling element bearings. Previous work has focused on the detection of anomalies using features taken directly from the time or frequency domain of the vibration signals to determine if a machine has a fault. In this work, I detect outliers using features derived from encoded vibration signals via sparse coding with dictionary learning. I investigate multiple outlier detection algorithms and evaluate their performance using different features taken from the sparse representation. I show that it is possible to detect an abnormal behavior on a bearing earlier than reported fault dates using typical condition monitoring systems.

Place, publisher, year, edition, pages
2019. , p. 73
Keywords [en]
machine learning, outlier detection, data analysis, rolling element bearing
Keywords [sv]
maskininlärning, dataanalys, kullager
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-76592OAI: oai:DiVA.org:ltu-76592DiVA, id: diva2:1367390
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level
Presentation
2019-09-12, A3583, Luleå Tekniska Universitet A-huset, Luleå, 08:00 (English)
Supervisors
Examiners
Available from: 2019-11-04 Created: 2019-11-03 Last updated: 2019-11-04Bibliographically approved

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7891011121310 of 68
CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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