Open Access
Issue
MATEC Web Conf.
Volume 95, 2017
2016 the 3rd International Conference on Mechatronics and Mechanical Engineering (ICMME 2016)
Article Number 13001
Number of page(s) 5
Section Mechatronics
DOI https://doi.org/10.1051/matecconf/20179513001
Published online 09 February 2017
  1. Jayaswal P., Verma S.N. and Wadhwani A.K., 2011. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. Journal of Vibration and Control, 17(8), pp.1131–1148. [CrossRef] [Google Scholar]
  2. Yiakopoulos C.T., Gryllias K.C. and Antoniadis I.A., 2011. Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Expert Systems with Applications, 38(3), pp.2888–2911. [CrossRef] [Google Scholar]
  3. Li Y., Xu M., Wei Y. and Huang W., 2016. A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree. Measurement, 77, pp.80–94. [CrossRef] [Google Scholar]
  4. Shao H., Jiang H., Zhang X. and Niu M., 2015. Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 26(11), p.115002. [CrossRef] [Google Scholar]
  5. Widodo A. and Yang B.S., 2007. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical systems and signal processing, 21(6), pp. 2560–2574. [CrossRef] [Google Scholar]
  6. Krizhevsky A., Sutskever I. and Hinton G.E., 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105). [Google Scholar]
  7. LeCun Y., Bengio Y. and Hinton G., 2015. Deep learning. Nature, 521(7553), pp.436–444. [CrossRef] [PubMed] [Google Scholar]
  8. Palaz D., Collobert R. and Doss M.M., 2013. Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks. arXiv preprint arXiv:1304.1018. [Google Scholar]
  9. Gerek O.N. and Ece D.G., 2004. 2-D analysis and compression of power-quality event data. IEEE Transactions on Power Delivery, 19(2), pp.791–798. [CrossRef] [Google Scholar]
  10. Kingma D. and Ba J., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). [Google Scholar]
  11. Ioffe S. and Szegedy C., Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, (2015). [Google Scholar]

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