MATEC Web Conf.
Volume 319, 20202020 8th Asia Conference on Mechanical and Materials Engineering (ACMME 2020)
|Number of page(s)||5|
|Section||Intelligent Manufacturing and Control Engineering|
|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. [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. [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. [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. [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. [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. [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. [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. [Google Scholar]
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