Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-022-04931-4.pdf
Reference60 articles.
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3. Li H, Huang J, Yang X, Luo J, Zhang L, Pang Y (2020) Fault diagnosis for rotating machinery using multiscale permutation entropy and convolutional neural networks. Entropy 22:851. https://doi.org/10.3390/e22080851
4. Razavi-Far R, Hallaji E, Farajzadeh-Zanjani M, Saif M, Kia SH, Henao H, Capolino G-A (2019) Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems. IEEE Trans Ind Electron 66:6331–6342
5. Li Y, Zou L, Jiang L, Zhou X (2019) Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network. IEEE Access 7:165710–165723. https://doi.org/10.1109/ACCESS.2019.2953490
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