Wind turbine rolling bearing fault diagnosis method based on enhanced morphological filtering and third-order cumulant diagonal slice spectrum

被引：0
作者：
Luo Y. [1 ]
Chen C. [1 ,2 ]
Zhao S. [1 ]
机构：
[1] School of Mechanical Engineering, Shenyang University of Technology, Shenyang
[2] Liaoning Engineering Center for Vibration and Noise Control, Shenyang
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 03期
关键词：
Fault diagnosis; Morphological filtering; Rolling bearing; Third-order cumulant diagonal slice spectrum; Wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2020-0577
中图分类号：
学科分类号：
摘要：
To solve the problem that the fault signal of the wind turbine rolling bearing is difficult to be identified by the strong background noise, a fault detection method based on the enhanced morphological filtering and the third-order cumulant diagonal slice spectrum is proposed. Firstly, this method constructs a new enhanced morphology operator (EMDO) based on the basic morphology operators. Then, the feature energy factor is used to select the optimal structural element scale of the EMDO operator. Finally, the third-order cumulant diagonal slice spectrum de-noising performance is used to further enhance the feature extraction ability of the EMDO operator. The results of simulation and comparison experiments show that the method proposed in this paper can effectively eliminate the interference caused by Gaussian white noise and enhance the extraction of fault feature information of the wind turbine rolling bearing. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：373 / 381
页数：8
相关论文
共 16 条
  • [1] GU X J, CHEN C Z., Fault extraction method of wind turbine bearing based on VMD and QPSO-SR, Acta energiae solaris sinica, 40, 10, pp. 2946-2952, (2019)
  • [2] TIAN J, WANG Y J, WANG Z., Fault diagnosis for rolling bearing based on EEMD and spatial correlation denoising, Chinese journal of scientific instrument, 39, 7, pp. 144-151, (2018)
  • [3] XIONG G L, HU J F, CHEN H., Rolling bearing fault impact feature enhancement based on spectral kurtosis and non-local means, Chinese journal of scientific instrument, 37, 10, pp. 2176-2184, (2016)
  • [4] CUI X L, WANG H J, XING J S, Et al., Rolling bearing fault diagnosis of generalized morphological filtering and VMD decomposition, Journal of electronic measurement and instrumentation, 32, 4, pp. 51-57, (2018)
  • [5] LI Y F, LIU J X, LIN J H., High speed train cardan shaft fault detection based on time-varying scale morphology filter, Journal of mechanical engineering, 54, 4, pp. 278-284, (2018)
  • [6] WU X T, YANG M, YUAN X H, Et al., Bearing fault diagnosis using EEMD and improved morphological filtering method based on kurtosis criterion, Journal of vibration and shock, 34, 2, pp. 38-44, (2015)
  • [7] ZHANG X L, ZHANG Q, QIN X R, Et al., Fault diagnosis method for rolling bearing based on ITD-morphological filter and Teager energy spectrum, Chinese journal of scientific instrument, 37, 4, pp. 788-795, (2016)
  • [8] LI Y F, ZUO M J, CHEN Y J, Et al., An enhanced morphology gradient product filter for bearing fault detection, Mechanical systems & signal processing, 109, pp. 166-184, (2018)
  • [9] YAN X A, LIU Y, JIA M P., Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings, Measurement, 147, (2019)
  • [10] YU J B, LI C F, LYU J X., Average combination difference morphological filter analysis on fault signals of rolling bearing, Journal of Zhejiang University(engineering science), 52, 10, pp. 1845-1853, (2018)
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