Ball Bearing Diagnosis Using Data Hybridisation in Supervised Machine Learning
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34190-8_19
Reference14 articles.
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2. Fadda, M.L., Moussaoui, A.: Hybrid SOM–PCA method for modeling bearing faults detection and diagnosis. J. Braz. Soc. Mech. Sci. Eng. 40(5), 1–8 (2018). https://doi.org/10.1007/s40430-018-1184-7
3. Farhat, M.H., et al.: Digital twin-driven machine learning: ball bearings fault severity classification. Meas. Sci. Technol. 32(4), 044006 (2021). https://doi.org/10.1088/1361-6501/abd280
4. Fowler, J.E.: The redundant discrete wavelet transform and additive noise. IEEE Signal Process. Lett. 12(9), 629–632 (2005). https://doi.org/10.1109/LSP.2005.853048
5. Lecture Notes in Computer Science;G Guo,2003
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