Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings-Reference-Cited by-同舟云学术

Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings

Published:2021-12 Issue:2 Volume:1 Page:207-219
ISSN:2767-1402
Container-title:International Journal of Mechanical System Dynamics
language:en
Short-container-title:Int Journal of Mech Sys Dyn

Author:

Zhao Le¹²^ORCID,Yang Shaopu¹²,Liu Yongqiang¹

Affiliation:

1. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang Hebei China

2. School of Traffic and Transportation Shijiazhuang Tiedao University Shijiazhuang Hebei China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/msd2.12019

Reference42 articles.

1. Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs

2. Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

3. A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery

4. A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis

5. Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis

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