Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Model-free identification of multiple periodic excitations and detection of structural anomaly using noisy response measurements

  • Ying, Z.G. (Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wang, Y.W. (Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Y.Q. (Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Xu, C. (Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2020.12.07
  • Accepted : 2021.04.28
  • Published : 2021.09.25
⟨ Previous Next ⟩

Abstract

Anomaly and damage detection is an important research topic in the field of structural health monitoring (SHM). It is in general difficult to establish a precise computational model and measure multiple dynamic loads for complex structures. Model-free identification methods using only response measurements are therefore highly desired. Based on second-order statistics blind separation (SOSBS), this study explores response-only blind excitation separation and structural feature extraction when the structure is subject to multiple periodic excitations. The proposed method proceeds with two steps: (i) a transformation to convert the measurement space to eigenspace with identity covariance matrix and compact the measurement dimension to independent source dimension; and (ii) joint diagonalization of covariances with various time shifts to determine the mixture features. Neither structural model nor measurement of excitations is required in this method, and the extracted mixture matrix representative of structural dynamic characteristics can be used for structural anomaly detection and damage diagnosis. Both numerical simulation of a 3-degree-of-freedom vibration system and experimental study of a 5-story physical structure are conducted to verify the proposed method.

Keywords

Acknowledgement

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant No. PolyU 152767/16E) and in part by a grant from the National Science Foundation of China (Grant No. 12072312). The authors would also like to appreciate the funding support by the Innovation and Technology Commission of the Hong Kong SAR Government (Grant No. K-BBY1).

References

  1. Abed-Meraim, K., Xiang, Y., Manton, J.H. and Hua, Y. (2001), "Blind source-separation using second-order cyclostationary statistics", IEEE Transact. Signal Process., 49(4), 694-701. https://doi.org/10.1109/78.912913
  2. Antoni, J. and Chauhan, S. (2013), "A study and extension of second-order blind source separation to operational modal analysis", J. Sound Vib., 332(4), 1079-1106. https://doi.org/10.1016/j.jsv.2012.09.016
  3. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties. I: Bayesian statistical framework", J. Eng. Mech., 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  4. Belouchrani, A. and Amin, M.G. (1998), "Blind source separation based on time-frequency signal representations", IEEE Transact. Signal Process., 46(11), 2888-2897. https://doi.org/10.1109/78.726803
  5. Belouchrani, A., Abed-Meraim, K., Cardoso, J.F. and Moulines, E. (1997), "A blind source separation technique using second-order statistics", IEEE Transact. Signal Process., 45(2), 434-444. https://doi.org/10.1109/78.554307
  6. Bouguerriou, N., Haritopoulos, M., Capdessus, C. and Allam, L. (2005), "Novel cyclostationarity-based blind source separation algorithm using second order statistical properties: theory and application to the bearing defect diagnosis", Mech. Syst. Signal Process., 19(6), 1260-1281. https://doi.org/10.1016/j.ymssp.2005.07.007
  7. Brewick, P.T. and Smyth, A.W. (2017), "Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods", Struct. Control Health Monitor., 24(6), e1921. https://doi.org/10.1002/stc.1921
  8. Brownjohn, J.M., De Stefano, A., Xu, Y.L., Wenzel, H. and Aktan, A.E. (2011), "Vibration-based monitoring of civil infrastructure: challenges and successes", J. Civil Struct. Health Monitor., 1(3-4), 79-95. https://doi.org/10.1007/s13349-011-0009-5
  9. Bull, L.A., Worden, K., Fuentes, R., Manson, G., Cross, E.J. and Dervilis, N. (2019), "Outlier ensembles: a robust method for damage detection and unsupervised feature extraction from high-dimensional data", J. Sound Vib., 453, 126-150. https://doi.org/10.1016/j.jsv.2019.03.025
  10. Carden, E.P. and Fanning, P. (2004), "Vibration based condition monitoring: a review", Struct. Health Monitor., 3(4), 355-377. https://doi.org/10.1177/1475921704047500
  11. Ciambella, J., Pau, A. and Vestroni, F. (2019), "Modal curvature-based damage localization in weakly damaged continuous beams", Mech. Syst. Signal Process., 121, 171-182. https://doi.org/10.1016/j.ymssp.2018.11.012
  12. Deraemaeker, A. and Worden, K. (2010), New Trends in Vibration Based Structural Health Monitoring, Springer, New York, USA.
  13. Dessi, D. and Camerlengo, G. (2015), "Damage identification techniques via modal curvature analysis: overview and comparison", Mech. Syst. Signal Process., 52, 181-205. https://doi.org/10.1016/j.ymssp.2014.05.031
  14. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: a review and comparative study", Struct. Health Monitor., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  15. Ghiasi, R., Ghasemi, M.R. and Chan, T.H. (2021), "Optimum feature selection for SHM of benchmark structures using efficient AI mechanism", Smart Struct. Syst., Int. J., 27(4), 623-640. https://doi.org/10.12989/sss.2021.27.4.623
  16. Giraldo, D.F., Song, W., Dyke, S.J. and Caicedo, J.M. (2009), "Modal identification through ambient vibration: comparative study", J. Eng. Mech., 135(8), 759-770. https://doi.org/10.1061/(ASCE)0733-9399(2009)135:8(759)
  17. Goyal, D. and Pabla, B.S. (2016), "The vibration monitoring methods and signal processing techniques for structural health monitoring: a review", Arch. Computat. Methods Eng., 23(4), 585-594. https://doi.org/10.1007/s11831-015-9145-0
  18. He, C., Li, H. and Zhao, X. (2018), "Weak characteristic determination for blade crack of centrifugal compressors based on underdetermined blind source separation", Measurement, 128, 545-557. https://doi.org/10.1016/j.measurement.2018.06.047
  19. Hyvarinen, A. and Oja, E. (2000), "Independent component analysis: algorithms and applications", Neural Networks, 13(4-5), 411-430. https://doi.org/10.1016/S0893-6080(00)00026-5
  20. Kay, S.M. (1984), "Accurate frequency estimation at low signal-to-noise ratio", IEEE Transact. Acoust. Speech Signal Process., 32(3), 540-547. https://doi.org/10.1109/TASSP.1984.1164358
  21. Kerschen, G., Poncelet, F. and Golinval, J.C. (2007), "Physical interpretation of independent component analysis in structural dynamics", Mech. Syst. Signal Process., 21(4), 1561-1575. https://doi.org/10.1016/j.ymssp.2006.07.009
  22. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  23. Li, H. and Ou, J. (2016), "The state of the art in structural health monitoring of cable-stayed bridges", J. Civil Struct. Health Monitor., 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x
  24. Li, H.N., Ren, L., Jia, Z.G., Yi, T.H. and Li, D.S. (2016), "State-of-the-art in structural health monitoring of large and complex civil infrastructures", J. Civil Struct. Health Monitor., 6(1), 3-16. https://doi.org/10.1007/s13349-015-0108-9
  25. Li, G., Tang, G., Wang, H. and Wang, Y. (2019), "Blind source separation of composite bearing vibration signals with low-rank and sparse decomposition", Measurement, 145, 323-334. https://doi.org/10.1016/j.measurement.2019.05.099
  26. 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
  27. Moughty, J.J. and Casas, J.R. (2017), "A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions", Appl. Sci., 7(5), 510. https://doi.org/10.3390/app7050510
  28. Musafere, F., Sadhu, A. and Liu, K. (2016), "Towards damage detection using blind source separation integrated with time-varying auto-regressive modeling", Smart Mater. Struct., 25(1), 015013. https://doi.org/10.1088/0964-1726/25/1/015013
  29. 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. http://dx.doi.org/10.12989/.2015.15.1.081
  30. Overschee, P.V. and Moor, B.D. (1996), Subspace Identification for Linear Systems: Theory Implementation Application, Kluwer Academic Publishers, Netherlands.
  31. Parra, L. and Sajda, P. (2003), "Blind source separation via generalized eigenvalue decomposition", J. Mach. Learn. Res., 4, 1261-1269. https://doi.org/10.1162/jmlr.2003.4.7-8.1261
  32. Peeters, B. and De Roeck, G. (2001), "Stochastic system identification for operational modal analysis: a review", J. Dyn. Syst. Measure. Control, 123(4), 659-667. https://doi.org/10.1115/1.1410370
  33. Prajapat, K. and Ray-Chaudhuri, S. (2018), "Detection of multiple damages employing best achievable eigenvectors under Bayesian inference", J. Sound Vib., 422, 237-263. https://doi.org/10.1016/j.jsv.2018.02.012
  34. Rainieri, C., Magalhaes, F., Gargaro, D., Fabbrocino, G. and Cunha, A. (2019), "Predicting the variability of natural frequencies and its causes by Second-Order Blind Identification", Struct. Health Monitor., 18(2), 486-507. https://doi.org/10.1177/1475921718758629
  35. Sadhu, A. and Narasimhan, S. (2014), "A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances", Struct. Control Health Monitor., 21(3), 282-302. https://doi.org/10.1002/stc.1558
  36. Sadhu, A., Narasimhan, S. and Antoni, J. (2017), "A review of output-only structural mode identification literature employing blind source separation methods", Mech. Syst. Signal Process., 94, 415-431. https://doi.org/10.1016/j.ymssp.2017.03.001
  37. Seo, J., Hu, J.W. and Lee, J. (2016), "Summary review of structural health monitoring applications for highway bridges", J. Perform. Constr. Facil., 30(4), 04015072. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000824
  38. Sohn, H. and Law, K.H. (1997), "A Bayesian probabilistic approach for structure damage detection", Earthq. Eng. Struct. Dyn., 26(12), 1259-1281. https://doi.org/10.1002/(SICI)1096-9845(199712)26:12<1259::AID-EQE709>3.0.CO;2-3
  39. Spencer, B.F., Ruiz-Sandoval, M.E. and Kurata, N. (2004), "Smart sensing technology: opportunities and challenges", Struct. Control Health Monitor., 11(4), 349-368. https://doi.org/10.1002/stc.48
  40. Tong, L., Soon, V.C., Huang, Y.F. and Liu, R. (1990), "AMUSE: a new blind identification algorithm", Proceeding of IEEE International Symposium on Circuits and Systems, New Orleans, LA, USA.
  41. Vanik, M.W., Beck, J.L. and Au, S.K. (2000), "Bayesian probabilistic approach to structural health monitoring", J. Eng. Mech., 126(7), 738-745. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(738)
  42. Vanlanduit, S., Parloo, E., Cauberghe, B., Guillaume, P. and Verboven, P. (2005), "A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties", J. Sound Vib., 284(3-5), 1033-1050. https://doi.org/10.1016/j.jsv.2004.07.016
  43. Weng, J.H., Loh, C.H. and Yang, J.N. (2009), "Experimental study of damage detection by data-driven subspace identification and finite-element model updating", J. Struct. Eng., 135(12), 1533-1544. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000079
  44. Wu, W.H., Jhou, J.W., Chen, C.C. and Lai, G. (2019), "A novel recursive stochastic subspace identification algorithm with its application in long-term structural health monitoring of office buildings", Smart Struct. Syst., Int. J., 24(4), 459-474. https://doi.org/10.12989/sss.2019.24.4.459
  45. Yang, Y.B. and Chen, W.F. (2016), "Extraction of bridge frequencies from a moving test vehicle by stochastic subspace identification", J. Bridge Eng., 21(3), 04015053. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000792
  46. Yang, Y. and Nagarajaiah, S. (2014), "Blind identification of damage in time-varying systems using independent component analysis with wavelet transform", Mech. Syst. Signal Process., 47(1-2), 3-20. https://doi.org/10.1016/j.ymssp.2012.08.029
  47. Yeredor, A. (2000), "Blind separation of Gaussian sources via second-order statistics with asymptotically optimal weighting", IEEE Signal Process. Lett., 7(7), 197-200. https://doi.org/10.1109/97.847367
  48. Ying, Z.G., Ni, Y.Q. and Kang, L. (2019), "Mode localization characteristics of damaged quasiperiodically supported beam structures with local weak coupling", Struct. Control Health Monitor., 26(6), e2351. https://doi.org/10.1002/stc.2351
  49. Zhang, F.L., Ni, Y.Q., Ni, Y.C. and Wang, Y.W. (2016), "Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method", Smart Struct. Syst., Int. J., 17(2), 209-230. http://dx.doi.org/10.12989/sss.2016.17.2.209