Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Information Systems,Building and Construction
Reference51 articles.
1. Y. Ding, L. Ma, J. Ma, M. Suo, L. Tao, Y. Cheng, C. Lu, Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach, Adv. Eng. Informatics, vol. 42, pp. 100977, October. 2019.
2. X. Li, W. Zhang, N. Xu, Q. Ding, Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places, IEEE Transactions on Industrial Electronics, vol. 67, pp. 6785-6794, August. 2020.
3. Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li and A. K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap, Mech. Syst. Signal Process., vol. 138, pp. 106587, April. 2020.
4. A subspace learning-based feature fusion and open-set fault diagnosis approach for machinery components;Tian;Adv. Eng. Inf.,2018
5. Y. Gao, D. Yu, Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs, Adv. Eng. Inform., vol. 47, pp. 101253, January. 2021.
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