Hybrid vibration signal monitoring approach for rolling element bearings
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
Date
2019Copyright
© The Authors, 2019
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.
Publisher
ESANNParent publication ISBN
978-2-87587-065-0Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningIs part of publication
ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-90.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/32124806
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Related funder(s)
Research Council of FinlandFunding program(s)
Academy Programme, AoF; Research profiles, AoFAdditional information about funding
The work supported by the Academy of Finland from grants 311877 and 315550.License
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