A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11465-022-0673-7.pdf
Reference41 articles.
1. Zhang X, Huang T, Wu B, Hu Y M, Huang S, Zhou Q, Zhang X. Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples. Frontiers of Mechanical Engineering, 2021, 16(2): 340–352
2. Chen X F, Wang S B, Qiao B J, Chen Q. Basic research on machinery fault diagnostics: past, present, and future trends. Frontiers of Mechanical Engineering, 2018, 13(2): 264–291
3. Lei Y G, Yang B, Jiang X W, Jia F, Li N P, Nandi A K. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing, 2020, 138: 106587
4. Nath A G, Udmale S S, Singh S K. Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artificial Intelligence Review, 2021, 54: 2609–2668
5. Wang J L, Xu C Q, Dai L, Zhang J, Zhong R Y. An unequal deep learning approach for 3-D point cloud segmentation. IEEE Transactions on Industrial Informatics, 2021, 17(12): 7913–7922
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