A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions-Reference-Cited by-同舟云学术

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A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

Published:2022-09-15 Issue:11 Volume:65 Page:2551-2563
ISSN:1674-7321
Container-title:Science China Technological Sciences
language:en
Short-container-title:Sci. China Technol. Sci.

Author:

Jin YanRui,Qin ChengJin,Zhang ZhiNan,Tao JianFeng,Liu ChengLiang

Publisher

Springer Science and Business Media LLC

Subject

General Engineering,General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s11431-022-2109-4.pdf

Reference52 articles.

1. Di Z Y, Shao H D, Xiang J W. Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions. Sci China Tech Sci, 2021, 64: 481–492

2. Huang H R, Li K, Su W S, et al. An improved empirical wavelet transform method for rolling bearing fault diagnosis. Sci China Tech Sci, 2020, 63: 2231–2240

3. Xu Y G, Wang L, Hu A J, et al. Time-extracting S-transform algorithm and its application in rolling bearing fault diagnosis. Sci China Tech Sci, 2022, 65: 932–942

4. Wang J, Li S, An Z, et al. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 2019, 329: 53–65

5. Cui L, Wu N, Ma C, et al. Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary. Mech Syst Signal Process, 2016, 68–69: 34–43

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