A main bearing fault feature enhancement method based on cyclical information extraction

被引：0
|
作者：
Luan X. [1 ]
Zhao J. [1 ]
Sha Y. [1 ]
Tong X. [1 ]
Zhang Z. [1 ]
机构：
[1] Key Laboratory of Advanced Measurement and Test Technique for Aviation Propulsion System, Liaoning Province, School of Aero-Engine, Shenyang Aerospace University, Shenyang
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2024年 / 45卷 / 03期
关键词：
aero engine; fault feature enhancement; feature information cycle extraction criteria; rolling bearings; wavelet packet decomposition;
D O I：
10.19650/j.cnki.cjsi.J2311990
中图分类号：
学科分类号：
摘要：
In response to the problem of insufficient feature information extraction when the main bearing of aircraft engine fails, a method for enhancing the fault characteristics of main bearings based on cyclic extraction of effective information is proposed. Firstly, the original vibration signals are decomposed using wavelet packet decomposition, and the correlation coefficient and kurtosis values of each node component are calculated and normalized, and then fused into a comprehensive parameter Pi. Secondly, a confidence interval is defined based on the feature information cyclic extraction criterion, which divides all node components into three parts: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high noise signals. Then, high signal-to-noise ratio signals are continuously selected until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, and envelope demodulation is performed to extract the weak fault characteristics of the bearings. Simulation signal verification shows that the signal-to-noise ratio of the denoised signal is improved by 11.31 dB compared to before denoising. The effectiveness of the feature information cyclic extraction method is comprehensively verified based on the data measured from a simulated test bench for intermediate shaft bearings in aircraft engines, and an analysis of the vibration signals of a certain type of aircraft engine main bearings is conducted. Practice shows that This method is suitable for feature extraction of rolling bearing under the condition of strong background noise interference, and can accurately diagnose the main bearing fault of aircraft engine. © 2024 Science Press. All rights reserved.
引用
收藏
页码：251 / 262
页数：11
相关论文
共 30 条
  • [21] LUAN X CH, SHA Y D, LIU G M, Et al., Accurate identification of early fault characteristics of rolling bearing based on WPD-KVI-Hilbert transform, Journal of Propulsion Technology, 43, 2, pp. 362-373, (2002)
  • [22] SHA Y D, ZHAO Y, LUAN X CH, Et al., Feature extraction and characterization method of rolling bearing Vibration Signal based on multi-parameter information fusion screening, Journal of Propulsion, 44, 7, pp. 243-253, (2019)
  • [23] SHA Y D, ZHAO J H, LUAN X CH, Et al., Fault feature extraction method of aircraft main bearing based on threshold parameter decision screening [J/OL], Journal of Aerodynamics, pp. 1-13
  • [24] LI ZH, SHA Y D, LUAN X CH, Et al., Weak fault feature extraction technology of intermediate bearing based on analog cartridge, Science Technology and Engineering, 21, 35, pp. 15262-15269, (2021)
  • [25] ZANG D Q, QIN L, HE W F, Et al., Fault characteristics identification of turbo pump bearings based on EMD-Hilbert envelope spectrum analysis, Rocket Propulsion, 49, 5, pp. 59-65, (2019)
  • [26] ZANG Y P, ZHANG D J, WANG W ZH, Wavelet layered threshold noise reduction method and its application in engine vibration signal analysis, Journal of Vibration and Shock, 28, 8, pp. 57-60, (2009)
  • [27] DONG CH H, GAO ZH, YU X H., Principle and application of Matlab wavelet analysis toolbox, (2004)
  • [28] ZHANG J, ZHANG J Q, ZHONG M, Et al., Weak fault diagnosis of fan bearing based on PSO-VMD-MCKD method, Journal of Vibration, Measurement and Diagnosis, 20, pp. 287-296
  • [29] LI Y ZH, LUAN X CH, YANG J, Et al., Vibration feature extraction and characterization method of rolling bearing based on Gray Wolf algorithm optimization with maximum correlation kurtosis deconvolution, Journal of Aerodynamics, pp. 1-18
  • [30] LUAN X CH, NA W X, SHA Y D, Et al., Bearing fault diagnosis method based on characteristic quantity threshold decision, Journal of Propulsion Technology, 43, 4, pp. 307-317, (2022)
123