A Bearing Life Prediction Method of Improving Smooth Degree and the Background Value

Yichi Zhang; Yan Xu; Tao Shu

doi:10.1051/e3sconf/202123602006

All issues

Volume 236 (2021)

E3S Web Conf., 236 (2021) 02006

References

Open Access

Issue		E3S Web Conf. Volume 236, 2021 3^rd International Conference on Energy Resources and Sustainable Development (ICERSD 2020)


Article Number		02006
Number of page(s)		5
Section		New Energy Technology and Clean Energy Production and Transformation
DOI		https://doi.org/10.1051/e3sconf/202123602006
Published online		09 February 2021

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[1] Wang H M, Xie D, Yao B, et al. Based on the backstepping theory of linear speed sensorless control of traction motor [J]. Proceedings of the Csee, 2018, 38 (22): 257-269. [Google Scholar]

[2] Yao X G, Guo X S, Feng Y B, et al. Since the missile vertical system simulation [J]. Journal of These, 2007. (01): 23-27. [Google Scholar]

[3] Zhao G L. Fault diagnosis and health management - power group and the bearing module analysis [D]. Dalian University of Technology, 2018. [Google Scholar]

[4] Zhong X, Liu W B, Yang J F. Rolling bearing performance degradation assessment based on logistic regression [J]. Journal of information science and technology, 2010, (16): 504-505. [Google Scholar]

[5] Zhong X. Based on logistic regression and Gaussian mixture model of equipment fault diagnosis technology research and application [D]. Beijing university of chemical industry, 2010. [Google Scholar]

[6] Bearing Data Set [EB/OL]. NASA Ames Prognostics data. Repository [2015-05-04]. http://ti.arc.nasa.gov/tech/dash/poe/prognostic-datarepository. [Google Scholar]

[7] Peng G Y. Bridge construction monitoring of the improved GM (1, 1) grey forecasting model study [J]. Journal of transportation science and engineering, 2019, 35 (01): 54-60. [8] Liu Jie-fang, et al. The Grey Prediction Model based on New Data Transformation Technology and Application [J]. Journal of mathematics practice and understanding, 2015, (01): 197-202. [Google Scholar]

[8] Wang Jia-wei, et al. Based on the Improved Grey Model to Predict GDP of Xinjiang [J]. Journal of mathematics practice and understanding, 2019,49 (17): 306-312. [Google Scholar]