Trend prediction of wear fault of wind generator high-speed gear using a fusion of UICA and PE method-Reference-Cited by-同舟云学术

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Trend prediction of wear fault of wind generator high-speed gear using a fusion of UICA and PE method

Published:2017-01-28 Issue:1 Volume:20 Page:427-437
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Zhao Xiwei,Xu Xiaoli,Zhao Wenxiang,Jiang Zhanglei,Liu Xiuli

Funder

National Natural Science Foundation of China

Key Project of Natural Science Foundation of Beijing City

National High Technology Research and Development Program

Project Supported by Beijing Municipal Education Commission

Key Project of Science and Technique Development Plan Supported by Beijing Municipal Commission of Education

Beijing Municipal Education Commission Science and Technology Plan Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/content/pdf/10.1007/s10586-017-0733-7.pdf

Reference25 articles.

1. Xu, X., Wang, H.: Large Rotating Machinery Running State Trend Forecasting. Science Press, Beijing (2011) (in Chinese)

2. Ar, M., Kar, C.: Fault detection in a multistage gearbox by demodulation of motor current waveform. IEEE Trans. Ind. Electron. 53(4), 1285–1297 (2006)

3. Casadei, D.: Diagnostic technique based on rotor modulating signals signature analysis for doubly fed induction machines in wind generator systems. In: Proceedings of IEEE IAS’06, Tampa, vol. 3, pp. 1525–1532 (2006)

4. Inalpolat, M., Kahraman, A.: A theoretical and experimental investigation of modulation sidebands of planetary gear sets. J. Sound Vib. 323, 677–696 (2009)

5. Dou, D., Zhou, S.: Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery. Appl. Soft Comput. 46, 459–468 (2016)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review of vibration-based gear wear monitoring and prediction techniques;Mechanical Systems and Signal Processing;2023-01

2. Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning;Energies;2019-12-12

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