Bearing fault diagnostic using machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s13748-020-00217-z.pdf
Reference39 articles.
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3. Henao, H., et al.: Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind. Electron. Mag. 8(2), 31–42 (2014)
4. Bonnett, A.H., Yung, C.: Increased efficiency versus increased reliability. IEEE Ind. Appl. Mag. 14(1), 29–36 (2008)
5. Wu, D., Wang, H., Liu, H., He, T., Xie, T.: Health monitoring on the spacecraft bearings in high-speed rotating systems by using the clustering fusion of normal acoustic parameters. Appl. Sci. 9(16), 3246 (2019)
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