FPGA Implementation of a Bearing Fault Classification System Based on an Envelope Analysis and Artificial Neural Network
Author:
Funder
direction générale de la recherche scientifique et du développement technologique
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-06599-7.pdf
Reference45 articles.
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2. Zhang, Y.; Ren, G.; Wu, D.; Wang, H.: Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method. Measurement 181, 109614 (2021). https://doi.org/10.1016/j.measurement.2021.109614
3. Duan, Z.; Wu, T.; Guo, S.; Shao, T.; Malekian, R.; Zhixiong, L.: Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review. Int. J. Adv. Manuf. Technol. 96, 803–819 (2018). https://doi.org/10.1007/s00170-017-1474-8
4. Tingarikar, G.; Choudhury, A.: Vibration analysis—based fault diagnosis of a dynamically loaded bearing with distributed defect. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-05862-7
5. Niu, X.; Zhu, L.; Ding, H.: New statistical moments for the detection of defects in rolling element bearings. Int. J. Adv. Manuf. Technol. 26, 1268–1274 (2005). https://doi.org/10.1007/s00170-004-2109-4
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