Blind source separation based vibration mode identification

被引：167
|
作者：
Zhou, Wenliang
Chelidze, David
机构：
[1] LLC, Lang Mekra N Amer, Ridgeway, SC 29130 USA
[2] Univ Rhode Isl, Dept Mech Engn & Appl Mech, Kingston, RI 02881 USA
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2007年 / 21卷 / 08期
基金：
美国国家科学基金会;
关键词：
modal analysis; Blind source separation; Independent component analysis; Vibration tests;
D O I：
10.1016/j.ymssp.2007.05.007
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In this paper, a novel method for linear normal mode (LNM) identification based on blind source separation (BSS) is introduced. Modal coordinates are considered as a specific case of sources that have certain time structure. This structure makes modal coordinates identifiable by many BSS algorithms. However, algorithms based on second-order statistics are particularly suited for extracting LNMs of a vibration system. Two well-known BSS algorithms are considered. First, algorithm for multiple unknown signals extraction (AMUSE) is used to illustrate the similarity with Ibrahim time domain (ITD) modal identification method. Second, second order blind identification (SOBI) is used to demonstrate noise robustness of BSS-based mode shape extraction. Numerical simulations and experimental results from these BSS algorithms and lTD method are presented. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：3072 / 3087
页数：16
相关论文
共 50 条
  • [1] Study on separation of the vibration response of diesel based on the blind source separation
    Li, Jun
    Zhang, Yong-Xiang
    Li, Lin
    Neiranji Gongcheng/Chinese Internal Combustion Engine Engineering, 2009, 30 (03): : 61 - 64
  • [2] Low Frequency Oscillation Mode Identification Based On Blind Source Separation Via Ambient Signals
    Lin, W. B.
    Ji, T. Y.
    Li, M. S.
    Wu, Q. H.
    PROCEEDINGS OF THE 2019 IEEE 12TH INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRICAL MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED), 2019, : 500 - 506
  • [3] Investigation of Modal Identification of Frame Structures Using Blind Source Separation Technique Based on Vibration Data
    Meng, Fanhao
    Ma, Yong
    Xia, Yongjun
    Ma, Yimin
    Jiang, Ming
    APPLIED SCIENCES-BASEL, 2023, 13 (12):
  • [4] Blind source separation for vibration-based diagnostics of rotorcraft bearings
    Haile, Mulugeta A.
    Dykas, Brian
    JOURNAL OF VIBRATION AND CONTROL, 2016, 22 (18) : 3807 - 3820
  • [5] An Improved Blind Source Separation for Structural Mode Identification Using Fewer Measurements
    Sadhu, Ayan
    Hazra, Budhaditya
    STRUCTURAL HEALTH MONITORING AND DAMAGE DETECTION, VOL 7, 2015, : 19 - 25
  • [6] A METHOD OF MODAL PARAMETERS IDENTIFICATION BASED ON BLIND SOURCE SEPARATION
    Zhang, X. D.
    Yao, Q. F.
    Liu, P.
    ENVIRONMENTAL VIBRATIONS: PREDICTION, MONITORING, MITIGATION AND EVALUATION, VOLS I AND II, 2009, : 1440 - 1445
  • [7] Canonical Polyadic Decomposition Based on a Single Mode Blind Source Separation
    Zhou, Guoxu
    Cichocki, Andrzej
    IEEE SIGNAL PROCESSING LETTERS, 2012, 19 (08) : 523 - 526
  • [8] Noise Source Separation based on the Blind Source Separation
    Yang, Yang
    Li, Zuoli
    Wang, Xiuqin
    Zhang, Di
    2011 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, 2011, : 2236 - +
  • [9] Blind Source Separation Method for Bearing Vibration Signals
    Jun, He
    Chen, Yong
    Zhang, Qing-Hua
    Sun, Guoxi
    Hu, Qin
    IEEE ACCESS, 2018, 6 : 658 - 664
  • [10] Blind source separation and blind equalization algorithms for mechanical signal separation and identification
    Tse, PW
    Zhang, JY
    Wang, XJ
    JOURNAL OF VIBRATION AND CONTROL, 2006, 12 (04) : 395 - 423
12345