Feature separation and extraction of compound faults of inner and outer rings of rolling bearings at variable speed based on order-frequency spectral coherence

被引:0
|
作者
Yang X. [1 ]
Guo Y. [1 ]
Hua J. [1 ]
机构
[1] Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming
来源
关键词
characteristic frequency band spectrum; compound faults; order-frequency spectral coherence; rolling element bearing; variable speed condition;
D O I
10.13465/j.cnki.jvs.2022.22.025
中图分类号
学科分类号
摘要
Aiming at the problem that compound faults arc coupled with each other and weak fault features are easy to be interfered and difficult to be identified under the condition of variable speed. A method of compound fault features separation and extraction based on the demodulation frequency band determination of order-frequency spectral coherence (OFSCoh) was proposed, which was then applied to compound faults diagnosis ol rolling bearings under variable speed condition. Firstly, the signal was calculated by the OFSCoh method. Then, the characteristic frequency band spectrum was obtained by integrating the OFSCoh in the failure order interval of the inner and outer race of the bearing, and the frequency corresponding to the maximum value in the characteristic frequency band spectrum was determined as the center frequency of the demodulation band, and the maximal speed frequency corresponding to 3 times of the fault frequency was taken as the bound of bandwidth. Finally the signal was filtered by a band pass, and the improved envelope spectrum (IES) was calculated, so as to realize the separation and extraction of bearing compound fault features. The simulation and experiment verify the effectiveness of the proposed method. © 2022 Chinese Vibration Engineering Society. All rights reserved.
引用
收藏
页码:211 / 218
页数:7
相关论文
共 14 条
  • [1] Cerrada M, Sanchez R V, Li C, Et al., A review on da-ta-driven fault severity assessment in rolling bearings[J], Mechanical Systems and Signal Processing, 99, 15, pp. 169-196, (2018)
  • [2] Gu J, Peng Y, Lu H, Et al., Compound fault diagnosis and identification of hoist spindle device based on hilbert huang and energy entropy, Journal of Mechanical Science and Technology, 35, 3, pp. 4281-4290, (2021)
  • [3] HU Aijun, BAI Zerui, ZHAO Jun, Compound fault features separation method of rolling bearing based on parameter op-timization VMD and 1.5 dimension spectrum[J], ournal of Vibration and Shock, 39, 11, pp. 50-57, (2020)
  • [4] ZHU Danchen, ZHANG Yongxiang, HE Wei, Et al., Com-pound faults diagnosis of rolling element bearing using adap-tive CYCBD and cross-correlation spectrum[J], Journal of Vibration and Shock, 39, 11, pp. 8-16, (2020)
  • [5] Hong L, Liu X, Zuo H., Compound faults diagnosis based on customized balanced multiwavelets and adaptive maximum correlated kurtosis deconvolution[J], Measure-ment, 146, 5, pp. 87-100, (2019)
  • [6] Pham Minh-Tuan, Kim Jong-Myon, Kim Cheol-Hong, 2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds[J], Ma-chines, 9, 199, pp. 199-210, (2021)
  • [7] Guo Y, Liu T, Et al., Envelope order tracking for fault detection in rolling element bearings[J], Journal of Sound and Vibration, 331, 25, pp. 5644-5654, (2012)
  • [8] Abboud D., Antoni, Et al., Order-frequency analysis of machine signals, Mechanical Systems and Signal Processing, 87, 21, pp. 229-258, (2017)
  • [9] XIA Junzhong, WANG Zhian, CHEN Chengfa, Et al., Fault feature extraction for rolling bearings based on integrated or-der-frequency spectral correlation[J], Journal of Vibration and Shock, 37, 23, pp. 86-91, (2018)
  • [10] He D, Wang X, Li S, Et al., Identification of multiple faults in rotating machinery based on minimum entropy de-convolution combined with spectral kurtosis [J], Mechanical Systems and Signal Processing, 81, 12, pp. 235-249, (2016)