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 editors: Kansanaho, Jarno; Kärkkäinen, Tommi

Parent publication: ESANN 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 conference: Bruges, Belgium, 24.-26.4.2019

ISBN: 978-2-87587-065-0

eISBN: 978-2-87587-066-7

Publication year: 2019

Pages range: 49-54

Number of pages in the book: 696

Publisher: ESANN

Publication country: Belgium

Publication language: English

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

Publication open access: Other 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.


Keywords: mechanical engineering; bearings; wear; oscillations; signal analysis; machine learning


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Related projects


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2021-28-06 at 17:09