A new health indicator extracted by unsupervised learning using autoencoder in tandem with t-sne and multi-kernel CNN to enhance the early detection and classification of bearings multi-faults
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,General Engineering,Aerospace Engineering,Automotive Engineering,Industrial and Manufacturing Engineering,Applied Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s40430-023-04307-6.pdf
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3. Pan L, Zhu D, She S, Song A, Shi X, Duan S (2018) Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion. Adv Mech Eng 10:1–10. https://doi.org/10.1177/1687814018811036
4. Li H, Zhang Y, Zheng H (2022) Fault classification with convolutional neural networks for microgrid systems. Int Trans Electrical Energy Syst, vol 2022, Article ID 8431450, 21 pages.
5. Ning S, Ren Y, Wu Y (2023) Intelligent fault diagnosis of rolling bearings based on the visibility algorithm and graph neural networks. J Braz Soc Mech Sci Eng 45:72. https://doi.org/10.1007/s40430-022-03913-0
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