Statistical asymptotic error on modal parameters in combined deterministic-stochastic identification algorithm

被引:6
|
作者
Raffy, M [1 ]
Gontier, C [1 ]
机构
[1] Univ Tours, Lab Mecan & Rheol, Tours Ecole Ingn Val Loire, F-41034 Blois, France
关键词
modal analysis; identification; subspace methods; statistics; confidence interval;
D O I
10.1016/j.ymssp.2004.11.001
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper deals with the estimation of the statistical dispersion of the modal parameters of a structure, frequencies and damping ratios obtained from subspace identification. The main objective is to apply, after some useful modifications, the theory of the covariance estimates of the poles to simulation and experimental cases and evaluate its performances in a real situation. A formulation of the asymptotic distribution of the dynamic matrix estimates given in literature is slightly modified to be directly interpretable in terms of accelerations. The method is extended to the modal parameters of a structure, which are non-linear functions of these estimates. The accuracy of the method is first analysed in simulation on the case of a spring-mass-damper system. Finally, the theory is applied to a real test case consisting of a reduced model of a two-floor building submitted to random excitation. (c) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:714 / 735
页数:22
相关论文
共 50 条
  • [1] Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification
    Wen, Peng
    Khan, Inamullah
    He, Jie
    Chen, Qiaofeng
    SHOCK AND VIBRATION, 2021, 2021
  • [2] Deterministic-Stochastic Subspace Identification of the Modal Parameters of a Machine Tool During Milling
    Reichert, Willy
    Kolouch, Martin
    Berthold, Jan
    Ihlenfeldt, Steffen
    Engelmann, Max
    JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING, 2025, 9 (02):
  • [3] Reference-based combined deterministic-stochastic subspace identification for operational modal analysis with deterministic inputs
    Reynders, E.
    De Roeck, G.
    PROCEEDINGS OF ISMA2006: INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING, VOLS 1-8, 2006, : 3035 - 3046
  • [4] Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis
    Reynders, Edwin
    De Roeck, Guido
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (03) : 617 - 637
  • [5] Subspace Identification of Combined Deterministic-Stochastic Systems by LQ Decomposition
    Katayama, Tohru
    2010 AMERICAN CONTROL CONFERENCE, 2010, : 2941 - 2946
  • [6] Combined deterministic-stochastic approach for pharmacokinetic modeling
    Lee, DY
    Sung, SW
    Lee, SY
    Park, S
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2004, 43 (04) : 1133 - 1143
  • [7] Dynamic force identification by means of state augmentation: a combined deterministic-stochastic approach
    Lourens, E.
    Reynders, E.
    Lombaert, G.
    De Roeck, G.
    Degrande, G.
    PROCEEDINGS OF ISMA2010 - INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING INCLUDING USD2010, 2010, : 2069 - 2080
  • [8] Combined deterministic-stochastic framework for modeling the agglomeration of colloidal particles
    Mortuza, S. M.
    Kariyawasam, Lahiru K.
    Banerjee, Soumik
    PHYSICAL REVIEW E, 2015, 92 (01):
  • [9] Combined Deterministic-Stochastic Online Subspace Identification for Power System Mode Estimation and Oscillation Classification
    Hossain, Sheikh Jakir
    Kamalasadan, Sukumar
    2019 IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING, 2019,
  • [10] Improving the numerical efficiency of the B and D estimates produced by the combined deterministic-stochastic subspace identification algorithms
    dos Santos, PL
    de Carvalho, JLM
    42ND IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-6, PROCEEDINGS, 2003, : 3473 - 3478