Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

被引:54
作者
Bhowmik, Basuraj [1 ]
Krishnan, Manu [2 ]
Hazra, Budhaditya [1 ]
Pakrashi, Vikram [3 ]
机构
[1] Indian Inst Technol Guwahati, Dept Civil Engn, Gauhati 781039, Assam, India
[2] Virginia Tech, Dept Aerosp & Ocean Engn, Blacksburg, VA USA
[3] Univ Coll Dublin, Sch Mech & Mat Engn, Dublin, Ireland
来源
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL | 2019年 / 18卷 / 02期
关键词
Recursive singular spectral analysis; time-varying autoregressive modeling; damage-sensitive features; structural health monitoring; real-time damage detection; IDENTIFICATION; DIAGNOSIS; VARIABILITY; ALGORITHM;
D O I
10.1177/1475921718760483
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A novel baseline-free approach for continuous online damage detection of multidegree of freedom vibrating structures using recursive singular spectral analysis in conjunction with time-varying autoregressive modeling is proposed in this article. The acceleration data are used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by time-varying autoregressive modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear/nonlinear states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focused on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection are missing that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The recursive singular spectral analysis algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order time-varying autoregressive models are applied on the transformed responses to improve detectability. Numerical simulations performed on a five-degree of freedom nonlinear system and on a single degree of freedom system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real-time, reference-free structural health monitoring.
引用
收藏
页码:563 / 589
页数:27
相关论文
共 47 条
  • [1] [Anonymous], 2012, MATRIX COMPUTATIONS
  • [2] Balageas D., 2006, Structural Health Monitoring
  • [3] Probabilistic identification of simulated damage on the Dowling Hall footbridge through Bayesian finite element model updating
    Behmanesh, Iman
    Moaveni, Babak
    [J]. STRUCTURAL CONTROL & HEALTH MONITORING, 2015, 22 (03) : 463 - 483
  • [4] Civil structure condition assessment by FE model updating: methodology and case studies
    Brownjohn, JMW
    Xia, PQ
    Hao, H
    Xia, Y
    [J]. FINITE ELEMENTS IN ANALYSIS AND DESIGN, 2001, 37 (10) : 761 - 775
  • [5] Vibration based condition monitoring: A review
    Carden, EP
    Fanning, P
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2004, 3 (04): : 355 - 377
  • [6] On the singular values decoupling in the Singular Spectrum Analysis of volcanic tremor at Stromboli
    Carniel, R.
    Barazza, F.
    Tarraga, M.
    Ortiz, R.
    [J]. NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2006, 6 (06) : 903 - 909
  • [7] Application of singular spectrum analysis to structural monitoring and damage diagnosis of bridges
    Chao, Shu-Hsien
    Loh, Chin-Hsiung
    [J]. STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2014, 10 (06) : 708 - 727
  • [8] Damage detection by means of structural damping identification
    Curadelli, R. O.
    Riera, J. D.
    Ambrosini, D.
    Amani, M. G.
    [J]. ENGINEERING STRUCTURES, 2008, 30 (12) : 3497 - 3504
  • [9] Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks
    Dackermann, Ulrike
    Smith, Wade A.
    Randall, Robert B.
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2014, 13 (04): : 430 - 444
  • [10] Doebling SW., 1998, Shock Vibr. Digest, V30, P91, DOI [10.1177/058310249803000201, DOI 10.1177/058310249803000201]