A4 Article in conference proceedings
Hybrid vibration signal monitoring approach for rolling element bearings (2019)


Kansanaho, J., & Kärkkäinen, T. (2019). Hybrid vibration signal monitoring approach for rolling element bearings. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 49-54). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-90.pdf


JYU authors or editors


Publication details

All authors or editorsKansanaho, Jarno; Kärkkäinen, Tommi

Parent publicationESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference:

  • European symposium on artificial neural networks, computational intelligence and machine learning

Place and date of conferenceBruges, Belgium24.-26.4.2019

ISBN978-2-87587-065-0

eISBN978-2-87587-066-7

Publication year2019

Pages range49-54

Number of pages in the book696

PublisherESANN

Publication countryBelgium

Publication languageEnglish

Persistent website addresshttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-90.pdf

Publication open accessOther way freely accessible online

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/66815


Abstract

New approach to identify different lifetime stages of rolling element bearings, to improve early bearing fault detection, is presented. We extract characteristic features from vibration signals generated by rolling element bearings. This data is first pre-labelled with an unsupervised clustering method. Then, supervised methods are used to improve the labelling. Moreover, we assess feature importance with each classifier. From the practical point of view, the classifiers are compared on how early emergence of a bearing fault is being suggested. The results show that all of the classifiers are usable for bearing fault detection and the importance of the features was consistent.


Keywordsmechanical engineeringoscillationsbearingssignal analysiswearmachine learning


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Ministry reportingYes

Reporting Year2019

JUFO rating1


Last updated on 2024-11-05 at 22:06