Open Access
Issue |
MATEC Web Conf.
Volume 319, 2020
2020 8th Asia Conference on Mechanical and Materials Engineering (ACMME 2020)
|
|
---|---|---|
Article Number | 03001 | |
Number of page(s) | 5 | |
Section | Intelligent Manufacturing and Control Engineering | |
DOI | https://doi.org/10.1051/matecconf/202031903001 | |
Published online | 10 September 2020 |
- D.-T. Hoang and H.-J. Kang: A survey on deep learning based bearing fault diagnosis, Neurocomputing, vol. 335, pp. 327-335, 2019. [CrossRef] [Google Scholar]
- D.-T. Hoang and H.-J. Kang: Rolling element bearing fault diagnosis using convolutional neural network and vibration image, Cognitive Systems Research, vol. 53, pp. 42-50, Advanced Intelligent Computing, 2019. [CrossRef] [Google Scholar]
- W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals, Sensors, vol. 17, no. 2, p. 425, 2017. [CrossRef] [Google Scholar]
- S. Guo, T. Yang, W. Gao, and C. Zhang: A novel fault diagnosis method for rotating machinery based on a convolutional neural network, Sensors, vol. 18, p. 14-29, 01 2018. [Google Scholar]
- X. Li, W. Zhang, Q. Ding, and J.-Q. Sun: Multi-layer domain adaptation method for rolling Bearing fault diagnosis, Signal Processing, vol. 157, pp. 180-197, 2019. [CrossRef] [Google Scholar]
- Q. Wang, G. Michau and O. Fink: Domain Adaptive Transfer Learning for Fault Diagnosis, 2019 Prognostics and System Health Management Conference (PHM-Paris), Paris, France, 2019, pp. 279-285. [CrossRef] [Google Scholar]
- Z. Zhao, Q. Zhang, X. Yu, C. Sun, S. Wang, R. Yan, and X. Chen: Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study, arXiv e-prints, p, arXiv: 1912.12528, Dec. 2019. [Google Scholar]
- M. Long, Y. Cao, J. Wang, and M. I. Jordan: Learning Transferable Features with Deep Adaptation Networks, arXiv e-prints, p. arXiv: 1502.02791, Feb. 2015. [Google Scholar]
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Ra-binovich: Going deeper with convolutions, CoRR, vol. abs/1409. 48-42, 2014. [Google Scholar]
- Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A: A kernel two-sample test. Journal of Machine Learning Research, 13:723-773, March 2012a. [Google Scholar]
- Elhamifar, E., & Vidal, R. (2013). Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2765-2781. [CrossRef] [Google Scholar]
- Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He: Multi-representation adaptation network for cross-domain image classification, Neural Networks, Volume 119, 2019, Pages 214-221. [CrossRef] [Google Scholar]
- D. P. Kingma and J. Ba: Adam: A Method for Stochastic Optimization, arXiv e-prints, p. arXiv: 1412.6980, Dec 2014. [Google Scholar]
- W. A. Smith and R. B. Randall: Rolling element bearing diagnostics using the case Western Reserve university data: A benchmark study, Mechanical Systems and Signal Processing, vol. 64-65, pp. 100-131, 2015. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.