Vibration source signal separation strategy of rotating machinery based on homology

被引：0
|
作者：
He Z. [1 ]
Liu D. [1 ]
Cheng W. [1 ]
机构：
[1] School of Mechanial, Electronic and Control Engineering, Beijing Jiaotong University, Beijing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 20期
关键词：
Fault diagnosis; Homology; Separation of vibration source signals; Separation strategy;
D O I：
10.13465/j.cnki.jvs.2021.20.006
中图分类号：
学科分类号：
摘要：
In order to solve the problem that it is difficult to determine the statistical characteristics and the number of vibration sources in the vibration source separation of rotating machinery, a vibration source separation strategy based on homologous response was proposed. The strategy establishes the description of the target object according to the repetitive characteristics of the rotating mechanical equipment, and summarizes the nature of the multiple response waveforms of the same vibration source (homologous) into three points: response fragmentation, similar patterns, and certain distribution rules. Taking the three properties of the homologous response as the separation criterion of the vibration source signals, it is more versatile for the vibration source signals of the rotating machinery, thereby overcoming the problems that the statistical characteristics of the vibration source signals and the number of sources are difficult to determine. Introducing the concept of homology, the vibration sources in the mixed signal were sequentially separated, which provides a new reference for the vibration source separation of rotating machinery. Based on this strategy, a method of separating vibration source signals was given, but it is not limited to the method given. The feasibility of this strategy has been verified by experimental analysis. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
引用
收藏
页码：42 / 49
页数：7
相关论文
共 25 条
  • [1] FORRESTER B D., Advanced vibration analysis techniques for fault detection and diagnosis in geared transmission systems, (1996)
  • [2] ZHAO D, WANG T, GAO R X, Et al., Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction, Mechanical Systems and Signal Processing, 134, (2019)
  • [3] HE Zhengjia, LIU Xiong, QU Liangsheng, The principle and application of signal time domain average, Signal Processing, 4, pp. 46-53, (1986)
  • [4] ANTONI J, RANDALL R B., The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems & Signal Processing, 20, 2, pp. 308-331, (2006)
  • [5] YU Jianbo, LIU Haiqiang, ZHENG Xiaoyun, Et al., Fault feature extraction method of rolling bearings based on ITD-SCS, Journal of Vibration and Shock, 37, 19, pp. 23-29, (2018)
  • [6] (2015)
  • [7] (2017)
  • [8] SAWADA H, ARAKI S, MUKAI R, Et al., Blind extraction of dominant target sources using ica and time-frequency masking, IEEE Transactions on Audio, Speech and Language Processing, 14, 6, pp. 2165-2173, (2006)
  • [9] JUTTEN C., Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, 24, 1, pp. 1-10, (1991)
  • [10] YPMA A, LESHEM A, DUIN R P W., Blind separation of rotating machine sources: bilinear forms and convolutive mixtures, Neurocomputing, 49, 1, pp. 349-368, (2002)
123