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
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2019
JUFO rating: 1
