Skip to main content
SpringerLink
Log in

Damage identification under ambient vibration and unpredictable signal nature

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

Ambient vibration is an unknown excitation source that may produce stationary or non-stationary signals. Under such circumstances, traditional feature extraction techniques may not yield relevant features to damage and not provide reliable results of damage identification. The main objective of this article is to propose a data-driven method based on the concept of statistical pattern recognition for locating damage under ambient vibration and unpredictable signal nature in terms of simultaneously stationary and non-stationary behavior. This method is generally comprised of a three-level hybrid algorithm for feature extraction and new statistical distance metrics for feature analysis. The proposed feature extraction method aims at providing new damage-sensitive features in three levels including (1) analyzing the nature of measured vibration signals in terms of stationarity or non-stationarity, and normalizing non-stationary signals by detrending and differencing techniques, (2) modeling each vibration signal by an Autoregressive Moving Average (ARMA) model along with extracting the model residuals, and (3) estimating the power spectral density of residual samples as a new spectral-based feature. To identify the location of damage via spectral-based features, this article proposes two new spectral-based measures called Jeffery’s and Smith’s distances. The major contributions of this study include proposing a new feature extraction method for dealing with the problem of unpredictable vibration nature and introducing two new distance metrics for damage identification. Experimental vibration measurements of a well-known laboratory structure are utilized to verify the proposed methods. Results demonstrate that these approaches succeed in accurately extracting relevant features and locating damage under ambient vibration and unpredictable signal nature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Mesquita E, Antunes P, Coelho F, André P, Arêde A, Varum H (2016) Global overview on advances in structural health monitoring platforms. J Civ Struct Health Monit 6(3):461–475. https://doi.org/10.1007/s13349-016-0184-5

    Article  Google Scholar 

  2. Chesné S, Deraemaeker A (2013) Damage localization using transmissibility functions: a critical review. Mech Syst Sig Process 38(2):569–584

    Article  Google Scholar 

  3. Kao C-Y, Chen X-Z, Jan JC, Hung S-L (2016) Locating damage to structures using incomplete measurements. J Civ Struct Health Monit 6(5):817–838. https://doi.org/10.1007/s13349-016-0202-7

    Article  Google Scholar 

  4. Jahangiri M, Hadianfard MA (2019) Vibration-based structural health monitoring using symbiotic organism search based on an improved objective function. J Civ Struct Health Monit 9(5):741–755. https://doi.org/10.1007/s13349-019-00364-5

    Article  Google Scholar 

  5. Sarmadi H, Entezami A, Ghalehnovi M (2020) On model-based damage detection by an enhanced sensitivity function of modal flexibility and LSMR-Tikhonov method under incomplete noisy modal data. Eng Comput. https://doi.org/10.1007/s00366-020-01041-8

    Article  Google Scholar 

  6. Ghahremani B, Bitaraf M, Rahami H (2020) A fast-convergent approach for damage assessment using CMA-ES optimization algorithm and modal parameters. J Civ Struct Health Monit 10(3):497–511. https://doi.org/10.1007/s13349-020-00397-1

    Article  Google Scholar 

  7. Farrar CR, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley, Chichester

    Google Scholar 

  8. Avendaño-Valencia LD, Fassois SD (2014) Stationary and non-stationary random vibration modelling and analysis for an operating wind turbine. Mech Syst Sig Process 47(1–2):263–285. https://doi.org/10.1016/j.ymssp.2013.07.022

    Article  Google Scholar 

  9. Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice-Hall, Bergen County

    MATH  Google Scholar 

  10. Goi Y, Kim C-W (2017) Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model. J Civ Struct Health Monit 7(2):153–162. https://doi.org/10.1007/s13349-017-0222-y

    Article  Google Scholar 

  11. Daneshvar MH, Gharighoran A, Zareei SA, Karamodin A (2021) Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods. J Civ Struct Health Monit. https://doi.org/10.1007/s13349-020-00466-5

    Article  Google Scholar 

  12. Gul M, Necati Catbas F (2011) Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. J Sound Vibrat 330(6):1196–1210

    Article  Google Scholar 

  13. Roy K, Bhattacharya B, Ray-Chaudhuri S (2015) ARX model-based damage sensitive features for structural damage localization using output-only measurements. J Sound Vibrat 349:99–122

    Article  Google Scholar 

  14. Bao C, Hao H, Li Z-X (2013) Integrated ARMA model method for damage detection of subsea pipeline system. Eng Struct 48:176–192. https://doi.org/10.1016/j.engstruct.2012.09.033

    Article  Google Scholar 

  15. Entezami A, Sarmadi H, Behkamal B, Mariani S (2020) Big data analytics and structural health monitoring: a statistical pattern recognition-based approach. Sensors 20(8):2328. https://doi.org/10.3390/s20082328

    Article  Google Scholar 

  16. Mei Q, Gul M (2016) A fixed-order time series model for damage detection and localization. J Civ Struct Health Monit 6(5):763–777. https://doi.org/10.1007/s13349-016-0196-1

    Article  Google Scholar 

  17. Mei L, Mita A, Zhou J (2016) An improved substructural damage detection approach of shear structure based on ARMAX model residual. Struct Contr Health Monit 23:218–236. https://doi.org/10.1002/stc.1766

    Article  Google Scholar 

  18. Entezami A, Shariatmadar H (2019) Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods. Struct Health Monit 18(2):347–375. https://doi.org/10.1177/1475921718754372

    Article  Google Scholar 

  19. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control, 5th edn. Wiley, Hoboken

    MATH  Google Scholar 

  20. Diez A, Khoa NLD, Alamdari MM, Wang Y, Chen F, Runcie P (2016) A clustering approach for structural health monitoring on bridges. J Civ Struct Health Monit 6(3):429–445

    Article  Google Scholar 

  21. Entezami A, Sarmadi H, Saeedi Razavi B (2020) An innovative hybrid strategy for structural health monitoring by modal flexibility and clustering methods. J Civ Struct Health Monit 10(5):845–859. https://doi.org/10.1007/s13349-020-00421-4

    Article  Google Scholar 

  22. Sarmadi H, Entezami A, Salar M, De Michele C (2021) Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. J Civ Struct Health Monit 11(3):629–644. https://doi.org/10.1007/s13349-021-00472-1

    Article  Google Scholar 

  23. Neves A, Gonzalez I, Leander J, Karoumi R (2017) Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Health Monit 7(5):689–702

    Article  Google Scholar 

  24. Ruffels A, Gonzalez I, Karoumi R (2020) Model-free damage detection of a laboratory bridge using artificial neural networks. J Civ Struct Health Monit 10(2):183–195. https://doi.org/10.1007/s13349-019-00375-2

    Article  Google Scholar 

  25. Entezami A, Sarmadi H, Mariani S (2020) An unsupervised learning approach for early damage detection by time series analysis and deep neural network to deal with output-only (big) data. Eng Proc 2(1):17. https://doi.org/10.3390/ecsa-7-08281

    Article  Google Scholar 

  26. Sarmadi H, Yuen K-V (2021) Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold. Copmut Aided Civil Infrastruct Eng. https://doi.org/10.1111/mice.12635

    Article  Google Scholar 

  27. Gharehbaghi VR, Nguyen A, Noroozinejad Farsangi E, Yang TY (2020) Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach. J Build Eng 30:101292. https://doi.org/10.1016/j.jobe.2020.101292

    Article  Google Scholar 

  28. Sarmadi H, Entezami A (2021) Application of supervised learning to validation of damage detection. Arch Appl Mech 91(1):393–410. https://doi.org/10.1007/s00419-020-01779-z

    Article  Google Scholar 

  29. Deza MM, Deza E (2013) Encyclopedia of distances, 3rd edn. Springer, Heidelberg

    Book  Google Scholar 

  30. Deraemaeker A, Worden K (2018) A comparison of linear approaches to filter out environmental effects in structural health monitoring. Mech Syst Sig Process 105:1–15. https://doi.org/10.1016/j.ymssp.2017.11.045

    Article  Google Scholar 

  31. Sarmadi H, Entezami A, Saeedi Razavi B, Yuen K-V (2021) Ensemble learning-based structural health monitoring by Mahalanobis distance metrics. Struct Contr Health Monit 28(2):e2663. https://doi.org/10.1002/stc.2663

    Article  Google Scholar 

  32. Kim Y, Park J-c, Shin S (2018) Development of a hybrid SHM of cable bridges based on the mixed probability density function. J Civ Struct Health Monit 8(4):569–583. https://doi.org/10.1007/s13349-018-0298-z

    Article  Google Scholar 

  33. Sarmadi H, Entezami A, Daneshvar Khorram M (2020) Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance. J Vibrat Control 26(11–12):1012–1027. https://doi.org/10.1177/1077546319891306

    Article  MathSciNet  Google Scholar 

  34. Sarmadi H, Karamodin A (2020) A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mech Syst Sig Process 140:106495. https://doi.org/10.1016/j.ymssp.2019.106495

    Article  Google Scholar 

  35. Eybpoosh M, Berges M, Noh HY (2017) An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions. Mech Syst Sig Process 82:260–278. https://doi.org/10.1016/j.ymssp.2016.05.022

    Article  Google Scholar 

  36. Mei Q, Gül M (2019) A crowdsourcing-based methodology using smartphones for bridge health monitoring. Struct Health Monit 18(5–6):1602–1619

    Article  Google Scholar 

  37. Beskhyroun S, Oshima T, Mikami S, Miyamori Y (2013) Assessment of vibration-based damage identification techniques using localized excitation source. J Civ Struct Health Monit 3(3):207–223. https://doi.org/10.1007/s13349-013-0043-6

    Article  Google Scholar 

  38. Liu Y, Wang X, Lin J, Kong X (2020) An adaptive grinding chatter detection method considering the chatter frequency shift characteristic. Mech Syst Sig Process 142:106672

    Article  Google Scholar 

  39. Ceravolo R, Lenticchia E, Miraglia G (2019) Spectral entropy of acceleration data for damage detection in masonry buildings affected by seismic sequences. Constr Build Mater 210:525–539

    Article  Google Scholar 

  40. Bisgaard S, Kulahci M (2011) Time series analysis and forecasting by example. Wiley, Hoboken

    Book  Google Scholar 

  41. Shi NZ, Tao J (2008) Statistical hypothesis testing: theory and methods. World Scientific Publishing

    Book  Google Scholar 

  42. Lehmann EL, Romano JP (2010) Testing statistical hypotheses. Springer, New York

    MATH  Google Scholar 

  43. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econometrics 54:159–178

    Article  Google Scholar 

  44. Leybourne SJ, McCabe BP (1994) A consistent test for a unit root. J Bus Econ Stat 12(2):157–166

    MathSciNet  Google Scholar 

  45. Warner RM (1998) Spectral analysis of time-series data. Guilford Publications

    Google Scholar 

  46. Entezami A, Shariatmadar H (2018) An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification. Struct Health Monit 17(2):325–345

    Article  Google Scholar 

  47. Yao R, Pakzad SN (2012) Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mech Syst Sig Process 31:355–368

    Article  Google Scholar 

  48. Castanié F (2013) Spectral analysis: parametric and non-parametric digital methods. Wiley

    MATH  Google Scholar 

  49. Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73

    Article  Google Scholar 

  50. Deborah H, Richard N, Hardeberg JY (2015) A comprehensive evaluation of spectral distance functions and metrics for hyperspectral image processing. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):3224–3234

    Article  Google Scholar 

  51. Dyke SJ, Bernal D, Beck J, Ventura C (2003) Experimental phase II of the structural health monitoring benchmark problem. In: The 16th ASCE engineering mechanics conference, Seattle, USA, 16–18 July 2003. American Sociaty of Civil Engineer (ASCE)

Download references

Author information

Authors and Affiliations

  1. Department of Construction and Mineral Engineering, Faculty of Technical and Engineering, Standard Research Institute, Tehran, Iran

    Behzad Saeedi Razavi

  2. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Mohammad Reza Mahmoudkelayeh

  3. School of Engineering and Information Technology, Conestoga College Institute of Technology and Advanced Learning Cambridge Campus, Kitchener, ON, Canada

    Shahrzad Saeedi Razavi

Authors
  1. Behzad Saeedi Razavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Mahmoudkelayeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahrzad Saeedi Razavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behzad Saeedi Razavi.

Ethics declarations

Conflict of interests

The authors would like to express that there is no conflict of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Razavi, B.S., Mahmoudkelayeh, M.R. & Razavi, S.S. Damage identification under ambient vibration and unpredictable signal nature. J Civil Struct Health Monit 11, 1253–1273 (2021). https://doi.org/10.1007/s13349-021-00503-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-021-00503-x

Keywords

Search

Navigation