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All issues Volume 309 (2020) MATEC Web Conf., 309 (2020) 03037 References
Open Access
Issue
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 03037
Number of page(s) 11
Section Smart Algorithms and Recognition
DOI https://doi.org/10.1051/matecconf/202030903037
Published online 04 March 2020
  1. LEI Yaguo, et al. “An intelligent fault diagnosis method usingunsupervised feature learning towards mechanical big data,”IEEE Transactions on Industrial Electronics, vol.63, Issue: 5, pp.3137–3147, May 2016. [CrossRef] [Google Scholar]
  2. Feng Jia, et al. “Deep neural networks: Apromising tool for fault characteristic mining and intelligent diagnosis of roating machinery with massive data,” Mechanical System and Signal Processing, Vol. 72-73, pp. 303–315, May 2016. [Google Scholar]
  3. Van Tung Tran, Failsal Al Thobiani, Andrew Ball. “An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks,” Expert Systems with Applications, Vol. 41, Issue 9, pp. 4113–4122, July 2014. [CrossRef] [Google Scholar]
  4. Haidong Shao, Hongkai Jiang, Xun Zhang, et al. “Rolling bearing fault diagnosis using an optimization deep belief network,” Measurement Science and Technology, Vol. 26, Issue 11, article id. 115002, November 2015. [Google Scholar]
  5. Wang Xingqing, Li Yanfeng, Ting Rui, et al. “Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network,” J of Vibroengineering, vol.17, Issue 3, pp. 1295–1308, May 2015. [Google Scholar]
  6. Wei D, Gong Q W, Lai W Q, et al. “Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network,”Proc of the Chinese Society for Electrical Engineering, Issue 36, pp. 36: 21-28, Sep.2016. [Google Scholar]
  7. Wenjun Sun, Siyu Shao, Rui Zhao, et al. “A sparse auto-encoder-based deep neural network approach for induction motor faults classification,” Measurement,Vol.89, pp. 171–178, July 2016. [CrossRef] [Google Scholar]
  8. Martin Langkvist, Lars Karlsson, Amy Loutfi. “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognition Letters, Vol. 42, Issue 1, pp.11–24, June 2014. [CrossRef] [Google Scholar]
  9. VINCENT P, LAROCHELLE H, LAJOIE I, et al. “Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion,” Journal of Machine Learning Research, Vol.11, Issue 12, pp.3371–3408, Jan. 2010. [Google Scholar]
  10. MASCI J, MEIER U, CIRE§AN D, et al. “Stacked convolutional auto-encoders for hierarchical feature extraction,” Proc. Of the 21st International Conference on Artificial Neural Networks, Espoo, Finland, Vol. Part I, pp. 52–59, June 2011. [Google Scholar]
  11. Hoyeop Lee, Youngju Kim, Chang Quk Kim. “A deep learning model for robust wafer fault monitoring with sensor measurement noise,” IEEE Trans on Semiconductor Manufacturing, Vol.30, Issue1, pp.23–31, Feb.2017. [CrossRef] [Google Scholar]
  12. DENG L, PLATT J C. “Ensemble deep learning for speech recognition,” Proc. of the Annual Conference of the International Speech Communication Association, Singapore, pp.1915–1919, Sep. 2014. [Google Scholar]
  13. PLATT J C. “Probabilistic Outputs for support vector machines and comparisons to regularized likelihood methods,” Advances in Large Margin Classifiers, Vol.10, Issue 4, pp. 61–74, Mar. 2000. [Google Scholar]
  14. Titijaroonroj, etal. “Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application,”IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, Vol.E100D, Issue 9, pp.2183–2196, Sep. 2017. [Google Scholar]

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