Analytical mode decomposition of time series with decaying amplitudes and overlapping instantaneous frequencies

被引:35
|
作者
Wang, Zuo-Cai [1 ]
Chen, Gen-Da [2 ]
机构
[1] Hefei Univ Technol, Dept Civil Engn, Hefei 23009, Anhui, Peoples R China
[2] Missouri Univ Sci & Technol, Dept Civil Architectural & Environm Engn, Rolla, MO 65401 USA
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
HILBERT-HUANG TRANSFORM; NATURAL FREQUENCIES; WAVELET TRANSFORMS; IDENTIFICATION; SYSTEMS; EARTHQUAKE; DAMPINGS; WIND; EMD;
D O I
10.1088/0964-1726/22/9/095003
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
In this study, the recently developed analytical mode decomposition with Hilbert transform was extended to the decomposition of a non-stationary and nonlinear signal with two or more amplitude-decaying and frequency-changing components. The bisecting frequency in the analytical mode decomposition became time-varying, and could be selected between any two adjacent instantaneous frequencies estimated from a preliminary wavelet analysis. The mathematical foundation for this new extension was integration of the bisecting frequency over time so that the original time series is actually decomposed in the phase domain. Parametric studies indicated that the analytically derived components are insensitive to the selection of bisecting frequency and the presence of up to 20% noise, sufficiently accurate when the sampling rate meets the Nyquist-Shannon sampling criterion, and applicable to both narrowband and wideband frequency modulations even when the signal amplitude decays over time. The proposed analytical mode decomposition is superior to the empirical mode decomposition and wavelet analysis in the preservation of signal amplitude, frequency and phase relations. It can be directly applied for system identification of buildings with time-varying stiffness.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Weighted Sliding Empirical Mode Decomposition for Online Analysis of Biomedical Time Series
    A. Zeiler
    R. Faltermeier
    A. M. Tomé
    C. Puntonet
    A. Brawanski
    E. W. Lang
    [J]. Neural Processing Letters, 2013, 37 : 21 - 32
  • [42] A Hybrid Model for Time Series Prediction Using Adaptive Variational Mode Decomposition
    Chen, Long
    Han, Zhongyang
    Zhao, Jun
    Liu, Ying
    Wang, Wei
    [J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 404 - 409
  • [43] THE MODIFIED EMPIRICAL MODE DECOMPOSITION METHOD FOR ANALYSING THE CYCLICAL BEHAVIOR OF TIME SERIES
    Sebesta, Vladimir
    Marsalek, Roman
    Pomenkova, Jitka
    [J]. PROCEEDINGS 27TH EUROPEAN CONFERENCE ON MODELLING AND SIMULATION ECMS 2013, 2013, : 288 - +
  • [44] Extracting trend of time series based on improved empirical mode decomposition method
    Liu, Hui-ting
    Ni, Zhi-wei
    Li, Jian-yang
    [J]. ADVANCES IN DATA AND WEB MANAGEMENT, PROCEEDINGS, 2007, 4505 : 341 - +
  • [45] Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series
    Zeiler, A.
    Faltermeier, R.
    Tome, A. M.
    Puntonet, C.
    Brawanski, A.
    Lang, E. W.
    [J]. Advances in Computational Intelligence, IWANN 2011, Pt I, 2011, 6691 : 299 - 306
  • [46] Ensemble Empirical Mode Decomposition for Time Series Prediction in Wireless Sensor Networks
    Goel, Gagan
    Hatzinakos, Dimitrios
    [J]. 2014 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC), 2014, : 594 - 598
  • [47] Weighted Sliding Empirical Mode Decomposition for Online Analysis of Biomedical Time Series
    Zeiler, A.
    Faltermeier, R.
    Tome, A. M.
    Puntonet, C.
    Brawanski, A.
    Lang, E. W.
    [J]. NEURAL PROCESSING LETTERS, 2013, 37 (01) : 21 - 32
  • [48] Tutorial on Empirical Mode Decomposition: Basis Decomposition and Frequency Adaptive Graduation in Non-Stationary Time Series
    van Jaarsveldt, Cole
    Peters, Gareth W.
    Ames, Matthew
    Chantler, Mike
    [J]. IEEE ACCESS, 2023, 11 : 94442 - 94478
  • [49] Empirical Mode Decomposition and Fourier analysis of Caspian Sea level's time series
    Dehghan, Yaser
    Sadrinasab, Masoud
    Chegini, Vahid
    [J]. OCEAN ENGINEERING, 2022, 252
  • [50] Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition
    Diez, Matteo
    Serani, Andrea
    Campana, Emilio F.
    Stern, Frederick
    [J]. JOURNAL OF OCEAN ENGINEERING AND MARINE ENERGY, 2022, 8 (04) : 471 - 478