Change search
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
Rolling element bearings diagnostics using the Symbolic Aggregate approXimation
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.ORCID iD: 0000-0001-9701-4203
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
Applied Mechanics Lab, Department of Mechanical Engineering and Aeronautics, University of Patras.
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
2015 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 60, p. 229-242Article in journal (Refereed) Published
Abstract [en]

Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 60, p. 229-242
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-66353DOI: 10.1016/j.ymssp.2015.01.033Scopus ID: 2-s2.0-84925970180OAI: oai:DiVA.org:ltu-66353DiVA, id: diva2:1154198
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Georgoulas, Georgios

Search in DiVA

By author/editor
Georgoulas, Georgios
In the same journal
Mechanical systems and signal processing
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 7 hits
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