A robust high-resolution method for the time-frequency analysis of vortex-induced-vibration signals

被引:3
|
作者
Hu, Sau-Lon James [1 ]
Yang, Wenlong [2 ,3 ]
Li, Huajun [4 ]
机构
[1] Univ Rhode Isl, Dept Ocean Engn, Narragansett, RI 02882 USA
[2] China Int Marine Containers Grp LTD, Shenzhen 518067, Peoples R China
[3] CIMC Offshore Engn Inst Co Ltd, Yantai 264670, Peoples R China
[4] Ocean Univ China, Shandong Prov Key Lab Ocean Engn, Qingdao 266100, Peoples R China
基金
中国国家自然科学基金;
关键词
Vortex-induced vibration; Time-frequency analysis; Short Time Prony Transform; State-space model; FLOW-INDUCED VIBRATION; CYLINDER;
D O I
10.1016/j.jfluidstructs.2014.08.013
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The dynamic interaction between ocean current and marine riser is complex in nature, and the riser's vortex-induced vibration (VIV) due to the current often strongly exhibits a non-stationary phenomenon. For investigating the time-varying dominant frequencies of the VIV motion, a joint time-frequency analysis is necessary. Traditional methods for time-frequency analysis include the Short Time Fourier Transform (STFT) and Wavelet Transform (WT) methods, though both methods have significant drawbacks. Specifically, the STFT method suffers frequency resolution and leakage problems, while the WT method is highly sensitive to its basic wavelet selection. This paper newly introduces a robust high-resolution method, named the STPT-SS method, which is the Short Time Prony Transform (STPT) using a State-Space (SS) model. In particular, the STPT algorithm contributes to the high-resolution feature of the proposed method, and the SS model to the robustness. Using test VIV data that include a synthesized signal and measurements from laboratory and field experiments, the STPT-SS method is found to significantly outperform the STFT and WT methods in the time-frequency analysis. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:255 / 280
页数:26
相关论文
共 50 条
  • [21] Designing high-resolution time-frequency and time-scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance
    Boashash, Boualem
    Ouelha, Samir
    DIGITAL SIGNAL PROCESSING, 2018, 77 : 120 - 152
  • [22] On timing in time-frequency analysis of speech signals
    Yegnanarayana, B
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 1996, 21 : 5 - 20
  • [23] Time-Frequency Analysis of Ionospheric Whistler Signals
    Dakovic, Milos
    Ponjavic, Milan
    Stankovic, Isidora
    Lerga, Jonatan
    Ioana, Cornel
    2019 27TH TELECOMMUNICATIONS FORUM (TELFOR 2019), 2019, : 285 - 288
  • [24] Time-Frequency Analysis of Suspension Bridge Response for Identification of Vortex Induced Vibrations
    Cantero, Daniel
    Oiseth, Ole
    Ronnquist, Anders
    EXPERIMENTAL VIBRATION ANALYSIS FOR CIVIL STRUCTURES: TESTING, SENSING, MONITORING, AND CONTROL, 2018, 5 : 667 - 675
  • [25] The Time-Frequency Analysis of Abnormal ECG Signals
    Song, Lantian
    Yu, Fengqin
    LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, 2010, 6330 : 60 - 66
  • [26] Framework of time-frequency analysis for vibration test
    Tian Guangming
    Chen Guangju
    Proceedings of the First International Symposium on Test Automation & Instrumentation, Vols 1 - 3, 2006, : 1856 - 1859
  • [27] High resolution time-frequency analysis for event-related electroencephalogram
    Hao, Dongmei
    Zhang, Hongwei
    Yu, Naigong
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 328 - 328
  • [28] A New Optimization Neural Network for High Resolution Time-Frequency Analysis
    ZS WangYSXiaJDZ ChenWHLiZYHeDept of Radio Engineering Southeast University Nanjing Dept of MathematicsNanjing University of Posts TelecommunicationsInstitute for Healthcare Researc Baptist Medical Center Oklahoma City
    电路与系统学报, 1997, (01) : 1 - 7
  • [29] Time-frequency characteristics of friction-induced vibration
    Tribology Research Institute, Southwest Jiaotong University, Chengdu 610031, China
    Jixie Gongcheng Xuebao, 2006, 2 (1-5): : 1 - 5
  • [30] High-resolution time-frequency representation of EEG data usingmulti-scale wavelets
    Li, Yang
    Cui, Wei-Gang
    Luo, Mei-Lin
    Li, Ke
    Wang, Lina
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2017, 48 (12) : 2658 - 2668