Browse > Article
http://dx.doi.org/10.12989/sss.2020.26.2.135

Online structural identification by Teager Energy Operator and blind source separation  

Ghasemi, Vida (Department of Civil Engineering, Iran University of Science and Technology)
Amini, Fereidoun (Department of Civil Engineering, Iran University of Science and Technology)
Publication Information
Smart Structures and Systems / v.26, no.2, 2020 , pp. 135-146 More about this Journal
Abstract
This paper deals with an application of adaptive blind source separation (BSS) method, equivariant adaptive separation via independence (EASI), and Teager Energy Operator (TEO) for online identification of structural modal parameters. The aim of adaptive BSS methods is recovering a set of independent sources from their unknown linear mixtures in each step when a new sample is received. In the proposed approach, firstly, the EASI method is used to decompose structural responses into independent sources at each instance. Secondly, the TEO based demodulation method with discrete energy separation algorithm (DESA-1) is applied to each independent source, and the instantaneous frequencies and damping ratios are extracted. The DESA-1 method can provide the fast time response and has high resolution so it is suitable for online problems. This paper also compares the performance of DESA-1 algorithm with Hilbert transform (HT) method. Compared to HT method, the DESA-1 method requires smaller amounts of samples to estimate and has a smaller computational complexity and faster adaption due to instantaneous characteristic. Furthermore, due to high resolution of the DESA-1 algorithm, it is very sensitive to noise and outliers. The effectiveness of the proposed approach has been validated using synthetic examples and a benchmark structure.
Keywords
adaptive blind source separation; discrete energy separation algorithm; equivariant adaptive separation via independence algorithm; online structural identification; Teager-Energy Operator;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Abazarsa, F., Nateghi, F., Ghahari, S.F. and Taciroglu, E. (2016), "Extended blind modal identification technique for nonstationary excitations and its verification and validation", J. Eng. Mech., 142(2), 04015078. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000990   DOI
2 Amini, F. and Ghasemi, V. (2018), "Adaptive modal identification of structures with equivariant adaptive separation via independence approach", J. Sound Vib., 413, 66-78. https://doi.org/10.1016/j.jsv.2017.09.033   DOI
3 Azam, S.E. and Mariani, S. (2018), "Online damage detection in structural systems via dynamic inverse analysis: A recursive Bayesian approach", Eng. Struct., 159, 28-45. https://doi.org/10.1016/j.engstruct.2017.12.031   DOI
4 Azam, S.E., Attari, N.K.A. and Mariani, S. (2017), "Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters", Nonlinear Dyn., 89(2), 1489-1511. https://doi.org/10.1007/s11071-017-3530-1   DOI
5 Azergui, M., Abenaou, A. and Bouzahir, H. (2018), "A Teager-Kaiser Energy Operator and Wavelet Packet Transform for Bearing Fault Detection", Smart Science, 6(3), 227-233. https://doi.org/10.1080/23080477.2018.1460892
6 Caicedo, J.M., Dyke, S.J. and Johnson, E.A. (2004), "Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: Simulated data", J. Eng. Mech., 130(1), 49-60. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(49)   DOI
7 Rageh, A., Azam, S.E. and Linzell, D.G. (2020), "Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty", Int. J. Fatigue, 134, 105458. https://doi.org/10.1016/j.ijfatigue.2019.105458   DOI
8 Rainieri, C. (2014), "Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures", Shock Vib., Article ID 845106, 9 pages. https://doi.org/10.1155/2014/845106
9 Ramazani, S. and Bahar, O. (2015), "EMD-based outputonly identification of mode shapes of linear structures", Smart Struct. Syst., Int. J., 16(5), 919-935. https://doi.org/10.12989/sss.2015.16.5.919   DOI
10 Sadhu, A., Hazra, B. and Narasimhan, S. (2014), "Ambient modal identification of structures equipped with tuned mass dampers using parallel factor blind source separation", Smart Struct. Syst., Int. J., 13(2), 257-280. https://doi.org/10.12989/sss.2014.13.2.257   DOI
11 Sadhu, A., Prakash, G. and Narasimhan, S. (2016), "A hybrid hidden Markov model towards fault detection of rotating components", J. Vib. Control, 23(19), 3175-3195. https://doi.org/10.1177/1077546315627934   DOI
12 Samadi, S., Babaie-Zadeh, M., Jutten, C. and Nayebi, K. (2004), "Blind source separation by adaptive estimation of score function difference", Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation, Granada, Spain, pp. 22-24. https://doi.org/10.1007/978-3-540-30110-3_2
13 Santos, A., Figueiredo, E., Silva, M.F.M., Sales, C.S. and Costa, J.C.W.A. (2016), "Machine learning algorithms for damage detection: Kernel-based approaches", J. Sound Vib., 363, 584-599. https://doi.org/10.1016/j.jsv.2015.11.008   DOI
14 Song, M., Astroza, R., Ebrahimian, H., Moaveni, B. and Papadimitriou, C. (2020), "Adaptive Kalman filters for 12nonlinear finite element model updating", Mech. Syst. Signal Process, 143, 106837. https://doi.org/10.1016/j.ymssp.2020.106837   DOI
15 Cao, M., Radzienski, M., Xu, W. and Ostachowicz, W. (2014), "Identification of multiple damages in beams based on robust curvature mode shapes", Mech. Syst. Signal Process., 46(2), 468-480. https://doi.org/10.1016/j.ymssp.2014.01.004   DOI
16 Cardoso, J.-F. and Laheld, B.H. (1996), "Equivariant adaptive source separation", IEEE Transection Signal Process., 44(2), 3017-3030. https://doi.org/10.1109/78.553476   DOI
17 Chase, J.G., Begoca, V. and Barroso, L. (2005a), "Efficient structural health monitoring for a benchmark structure using adaptive RLS filters", Comput. Struct., 83(8-9), 639-647. https://doi.org/10.1016/j.compstruc.2004.11.005   DOI
18 Chase, J.G, Leo Hwang, K., Barroso, L.R. and Mander, J.B. (2005b), "A simple LMS-based approach to the structural health monitoring benchmark problem", J. Earthq. Eng. Struct. Dyn. (EESD), 34(6), 575-594. https://doi.org/10.1002/eqe.433   DOI
19 Chu, S.-Y. and Lo, S.‐C, (2011), "Application of the on-line recursive least-squares method to perform structural damage assessment", Struct. Control Health Monit., 18(3), 241-264. https://doi.org/10.1002/stc.362   DOI
20 Comon, P. (1989), "Separation of stochastic processes", Proceedings of the Workshop Higher Order Spectral Analaysis, Vail, CO, USA, pp. 174-179. https://doi.org/10.1109/HOSA.1989.735291
21 Dimitriadis, D. and Maragos, P. (2006), "Continuous energy demodulation methods and application to speech analysis", Speech Commun., 48(7), 819-837. https://doi.org/10.1016/j.specom.2005.08.007   DOI
22 Feldman, M. (2011), "Hilbert transform in vibration analysis", Mech. Syst. Signal Process., 25(3), 735-802. https://doi.org/10.1016/j.ymssp.2010.07.018   DOI
23 Ghahari, S.F., Abazarsa, F. and Taciroglu, E. (2017), "Blind modal identification of non‐classically damped structures under nonstationary excitations", Struct. Control Health Monit., 24(6), e1925. https://doi.org/10.1002/stc.1925   DOI
24 Sorouchyari, E. (1991), "Blind separation of sources, part III: Stability analysis", Signal Process., 24(1), 21-29. https://doi.org/10.1016/0165-1684(91)90081-S   DOI
25 Ulriksen, M.D. and Damkilde, L. (2016), "Structural damage localization by outlier analysis of signal-processed mode shapes - Analytical and experimental validation", Mech. Syst. Signal Process., 68-69, 1-14. https://doi.org/10.1016/j.ymssp.2015.07.021   DOI
26 Ye, J. and Jin, H. (2009), "An optimized EASI algorithm", Signal Process., 89(3), 333-338. https://doi.org/10.1016/j.sigpro.2008.08.015   DOI
27 Zarzoso, V. and Nandi, A.K. (2000), "Adaptive blind source separation for virtually any source probability density function", IEEE Transection Signal Process., 48, 477-488. https://doi.org/10.1109/78.823974   DOI
28 Guo, Y. and Kareem, A. (2016a), "System identification through nonstationary data using time-frequency blind source separation", J. Sound Vib., 371, 110-131. https://doi.org/10.1016/j.jsv.2016.02.011   DOI
29 Guo, Y. and Kareem, A. (2016b), "Non-stationary frequency domain system identification using time-frequency representations", Mech. Syst. Signal Process., 72-73, 712-726. https://doi.org/10.1016/j.ymssp.2015.10.031   DOI
30 Hazra, B., Roffel, A.J., Narasimhan, S. and Pandey, M.D. (2010), "Modified cross-correlation method for the blind identification of structures", J. Eng. Mech., 136(7), 889-897. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000133   DOI
31 James, G.H., Carne, T.G. and Lauffer, J.P. (1993), "The natural excitation technique for modal parameter extraction from operating wind turbines", Int. J. Anal. Experim. Modal Anal., 10(4).
32 Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech., 130(1), 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)   DOI
33 Jutten, C. and Herault, J. (1991), "Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture", Signal Process., 24(1), 1-10. https://doi.org/10.1016/0165-1684(91)90079-X   DOI
34 Kaiser, J.F. (1990), "On a simple algorithm to calculate the 'energy' of a signal", Proceedings of IEEE ICASSP-90, Albuquerque, NM, USA, April. https://doi.org/10.1109/ICASSP.1990.115702
35 Kaiser, J. (1993), "Some useful properties of Teager's energy operators", Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, MN, USA, May. https://doi.org/10.1109/ICASSP.1993.319457
36 Lagunas Hernandez, M.A. (1994), "A general adaptive algorithm for nongaussian source separation without any constraint", Proceedings of EUSIPCO-94, 7th European Signal Processing Conference, Edinburgh, UK, pp. 1161-1164.
37 Liang, M. and Bozchalooi, I.S. (2010), "An energy operator approach to joint application of amplitude and frequencydemodulations for bearing fault detection", Mech. Syst. Signal Process., 24(5), 1473-1494. https://doi.org/10.1016/j.ymssp.2009.12.007   DOI
38 Loh, C.H., Weng, J.H., Liu, Y.C., Lin, P.Y. and Huang, S.K. (2011), "Structural damage diagnosis based on on-line recursive stochastic subspace identification", Smart Mater. Struct., 20(5). https://doi.org/10.1002/stc.362
39 Loh, C.-H., Liu, Y.-C. and Ni, Y.-Q. (2012), "SSA-based stochastic subspace identification of structures from output-only vibration measurements", Smart Struct. Syst., Int. J., 10(4), 331-351. https://doi.org/10.12989/sss.2012.10.4_5.331   DOI
40 Lus, H., Betti, R., Yu, J. and De Angelis, M. (2004), "Investigation of a system identification methodology in the context of the ASCE benchmark problem", J. Eng. Mech., 130(1), 71-84. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(71)   DOI
41 Maragos, P., Kaiser, J. and Quatieri T. (1993), "Energy separation in signal modulations with application to speech analysis", IEEE Transactions on Signal Process., 41(10), 3024-3051. https://doi.org/10.1109/78.277799   DOI
42 McNeill, S.I. and Zimmerman, D.C. (2008), "A framework for blind modal identification using joint approximate diagonalization", Mech. Syst. Signal Process., 22(7), 1526-1548. https://doi.org/10.1016/j.ymssp.2008.01.010   DOI
43 Nagarajaiah, S. and Yang, Y. (2015), "Blind modal identification of output-only non-proportionally-damped structures by timefrequency complex independent component analysis", Smart Struct. Syst., Int. J., 15(1), 81-97. https://doi.org/10.12989/.2015.15.1.081   DOI
44 Poncelet, F., Kerschen, G. and Golinval, J.-C. (2007), "Outputonly modal analysis using blind source separation techniques", Mech. Syst. Signal Process., 21(6), 2335-2358. https://doi.org/10.1016/j.ymssp.2006.12.005   DOI