Funder
NSFC, China
Committee of Science and Technology, Shanghai, China
Subject
Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering
Reference43 articles.
1. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks;Xia;IEEE/ASME Trans. Mechatron.,2017
2. S. Zhang, S. Zhang, B. Wang, T.G. Habetler, Machine learning and deep learning algorithms for bearing fault diagnostics-a comprehensive review, arXiv preprint arXiv:1901.08247.
3. Artificial intelligence for fault diagnosis of rotating machinery: a review;Liu;Mech. Syst. Signal Process.,2018
4. Y.O. Lee, J. Jo, J. Hwang, Application of deep neural network and generative adversarial network to industrial maintenance: a case study of induction motor fault detection, in: 2017 IEEE International Conference on Big Data (Big Data), IEEE, 2017, pp. 3248–3253.
5. An integrated ensemble learning model for imbalanced fault diagnostics and prognostics;Wu;IEEE Access,2018
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