Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method

被引：0
|
作者：
Cao S. [1 ,2 ]
Zheng J. [1 ,2 ]
Pan H. [1 ,2 ]
Tong J. [1 ,2 ]
Liu Q. [1 ,2 ]
机构：
[1] Special Heavy Duty Robot, Anhui Key Laboratory, Maanshan
[2] School of Mechanical Engineering, Anhui University of Technology, Maanshan
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 15期
关键词：
adaptive empirical Fourier decomposition (AEFD); empirical mode decomposition (EMD); envelope entropy; fault diagnosis; rolling bearing;
D O I：
10.13465/j.cnki.jvs.2022.15.036
中图分类号：
学科分类号：
摘要：
Adaptive empirical Fourier decomposition (AEFD) was a recently proposed non-stationary signal decomposition method. Here, to solve the problem of setting segmentation boundary set of AEFD, an enhanced adaptive empirical Fourier decomposition (EAEFD) method based on spectrum envelope detection is proposed. In this method, fast Fourier transform is taken as the base, the optimal number of decomposition modes is chosen by minimizing envelope entropy, and the maximum envelope technique is used to divide Fourier spectrum to obtain a reasonable segmentation boundary. Finally, the inverse fast Fourier transform is used to reconstruct each interval signal. EAEFD could adaptively decompose a complex signal into the sum of several single-component signals with instantaneous frequencies having physical meaning. Through analyzing simulated signals and rolling bearing signals, EAEFD method is compared with EWT, EMD, LCD and AEFD. The results show that EAEFD method can not only effectively diagnose fault features of rolling bearing, but also have higher diagnosis accuracy. © 2022 Chinese Vibration Engineering Society. All rights reserved.
引用
收藏
页码：287 / 299
页数：12
相关论文
共 20 条
  • [1] HUANG HR, UK SUWS, An improved empirical wavelet transform method for rolling bearing fault diagnosis, Science China Technological Sciences, 63, 11, pp. 2231-2240, (2020)
  • [2] CHOE H C, WAN Y L, CHAN A K., Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features, Proceedings of SPIE-The International Society for Optical Engineering, 3078, pp. 480-496, (1997)
  • [3] WU Bin, FENG Changjian, LUO Yuegang, Et al., A hybrid diagnosis method based on vibration signals for rolling bearing fault [J], Machinery Design & Manufacture, 11, pp. 178-179, (2009)
  • [4] FENG Z P, LIANG M, CHU F L., Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples, Mechanical Systems and Signal Processing, 38, 1, pp. 165-205, (2013)
  • [5] CHENG J S, YU D J, YANG Y., Time-energy density analysis based on wavelet transform [J], NDT & E International, 38, 7, pp. 569-572, (2005)
  • [6] YIN Aijun, LI Haizhu, LI Jiang, Et al., Evaluation and application of Wigner-Ville distribution complex wavelet similarity [J], Journal of Vibration, Measurement & Diagnosis, 40, pp. 7-11, (2020)
  • [7] CHUI C K, JIANG Q T, LI L, Et al., Analysis of an adaptive short-time Fourier transform-based multicomponent signal separation method derived from linear chirp local approximation [J], Journal of Computational and Applied Mathematics, 396, (2021)
  • [8] HUANG N E, SHEN Z, LONG S R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J], Proceedings of the Royal Society of London, 454, 1, pp. 903-995, (1998)
  • [9] HUANG N E, SHEN Z, LONG S R., A new view of nonlinear water waves: the Hilbert spectrum [J], Annual Review of Fluid Mechanics, 31, pp. 417-457, (1999)
  • [10] HUANG NE.WUZ., A review on Hilbert-Huang transform
12