Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning-Reference-Cited by-同舟云学术

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Published:2021-08-31 Issue: Volume: Page:
ISSN:2095-0233
Container-title:Frontiers of Mechanical Engineering
language:en
Short-container-title:Front. Mech. Eng.

Author:

Liu Jie,Zhou Kaibo,Yang Chaoying,Lu Guoliang

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11465-021-0652-4.pdf

Reference40 articles.

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3. Lei Y, Jia F, Lin J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical Big Data. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137–3147

4. Chen Z, Mauricio A, Li W, et al. A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mechanical Systems and Signal Processing, 2020, 140: 106683

5. Han T, Li Y, Qian M. A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–11

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