Bearing Fault Diagnosis Based on Compressed Data and Supervised Global-Local/Nonlocal Discriminant Analysis
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26193-0_97
Reference21 articles.
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3. Shao, H.D., Jiang, H.K., Zhang, H.Z., Duan, W.J., Liang, T.C., Wu, S.P.: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process. 100, 743–765 (2018)
4. Sun, J.D., Yan, C.H., Wen, J.T.: Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning. IEEE Trans. Instrum. Meas. 67(1), 185–195 (2018)
5. Shi, P., Guo, X., Han, D., Fu, R.: A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis. J. Mech. Sci. Technol. 34(4), 1445–1458 (2020). https://doi.org/10.1007/s12206-020-0306-1
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