Skip to main content
SpringerLink
Log in

Output-only damage localization technique using time series model

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

In this paper, we present a technique to detect the time instant and location of damage in civil structures using scalar time series models, by handling operational variability and measurement noise. The scalar Autoregressive (AR) and Autoregressive with exogenous inputs (ARX) models are used to obtain the time instant of damage and its spatial location. The spatial damage feature to locate the damage is obtained using a metric constructed from the probability density values of the prediction errors of AR–ARX model. The proposed method does not resort to any computationally expensive vector time series models to locate the damage and so highly preferable in smart wireless online continuous SHM schemes. Numerical simulation studies are carried out by using a simply supported beam model. The results of the studies indicate that the proposed technique is capable of identifying both the time instant and location of damage accurately using the proposed PDF based damage index. In order to validate the proposed technique with experimental results, the time-history data from the three-story bookshelf benchmark structure of EI-LANL is used. Finally, the laboratory experimental studies carried out on an RCC simply supported beam with inflicted damage are also presented. The experimental studies clearly indicate the effectiveness of the proposed damage index to detect the location of damage, by handling operational variability and measurement noise.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

References

  1. Lifshitz J M and Rotem A 1969 Determination of reinforcement unbonding of composites by a vibration technique J. Compos. Mater. 3(3): 412–423

    Article  Google Scholar 

  2. Plankis A 2012 Structural health monitoring MEMS sensors using elasticity-based beam vibrations. Ph.D thesis, Colorado State University, Fort Collins, CO, USA

  3. West W M 1986 Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen In: Proceedings of the Air Force Conference on Aircraft Structural Integrity, pp. 1–6

  4. Pandey A K, Biswas M and Samman M M 1991 Damage detection from changes in curvature mode shapes. J. Sound Vib. 145(2): 321–332

    Article  Google Scholar 

  5. Mottershead J E and Friswell M I 1993 Model updating in structural dynamics: a survey. J. Sound Vib. 167(2): 347–375

    Article  MATH  Google Scholar 

  6. Rytter A and Kirkegaard P 1997 Vibration based inspection using neural networks. In: Structural Damage Assessment Using Advanced Signal Processing Procedures, Proceedings of DAMAS ‘97, University of Sheffield, UK, 97–108

  7. Farrar C R and Jauregui D A 1998 Comparative study of damage identification algorithms applied to a bridge: I. Experiment. Smart Mater. Struct. 7(5): 704–719

    Article  Google Scholar 

  8. Huth O, Feltrin G, Maeck J, Kilic N and Motavalli M 2005 Damage identification using modal data: experiences on a prestressed concrete bridge. J. Struct. Eng. 131(12): 1898–1910.

    Article  Google Scholar 

  9. Limongelli M P 2010 Frequency response function interpolation for damage detection under changing environment. Mech. Syst. Signal Process. 24(8): 2898–2913

    Article  Google Scholar 

  10. Huang C C and Loh C H 2001 Nonlinear identification of dynamic systems using neural networks. Comput. Aided Civ. Infrastruct. Eng. 16(1): 28–41

    Article  Google Scholar 

  11. Shu J, Zhang Z, Gonzalez I and Karoumi R 2013 The application of a damage detection method using artificial neural network and train-induced vibrations on a simplified railway bridge model. Eng. Struct. 52: 408–421

    Article  Google Scholar 

  12. Kijewski T and Kareem A 2003 Wavelet transforms for system identification in civil engineering. Comput. Aided Civ. Infrastruct. Eng. 18(5): 339–355

    Article  Google Scholar 

  13. Huang G, Su Y, Kareem A and Liao H 2015 Time-frequency analysis of nonstationary process based on multivariate empirical mode decomposition. J. Eng. Mech. 142(1): 04015065

    Article  Google Scholar 

  14. Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N, Tung C and Liu H 1998 The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 454: 903–995

    Article  MathSciNet  MATH  Google Scholar 

  15. Giulio S, Francesco L, Riccardo T, Francesca G, Aurelio L C, Domenico L C, Mario C, Domenico G and Giovanni F 2016 A framework for the damage evaluation of acoustic emission signals through Hilbert–Huang transform. Mech. Syst. Signal Process. 75: 109–122

    Article  Google Scholar 

  16. Pyayt A L, Kozionov A P, Mokhov I I, Lang B, Meijer R J, Krzhizhanovskaya V V and Sloot P M A 2014 Time-frequency methods for structural health monitoring. Sensors 14(3): 5147–5173

    Google Scholar 

  17. Fassois S D and Sakellariou J S 2007 Time-series methods for fault detection and identification in vibrating structures. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 365(1851): 411–448

    Article  MathSciNet  Google Scholar 

  18. Box G E P, Jenkins G M and Reinsel G C 1994 Time series analysis: forecasting and control, 3rd edn. Englewood Cliffs, NJ: Prentice-Hall

    MATH  Google Scholar 

  19. Fuller W A 2009 Introduction to statistical time series, vol. 428. Hoboken, New Jersey, United States: John Wiley & Sons

    Google Scholar 

  20. Pandit S M and Wu S M 1983 Time series and system analysis with applications. New York, USA: Wiley

  21. de Lautour O R and Omenzetter P 2010 Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech. Syst. Signal Process. ISSN 0888-3270, 24(5): 1556–1569

  22. Wang Z and Ong K C G 2009 Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart. Eng. Struct. 31: 1265–1275

    Article  Google Scholar 

  23. Yu L and Zhu J H 2015 Nonlinear damage detection using higher statistical moments of structural responses. Struct. Eng. Mech. 54(2): 221–237.

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  25. Peeters B and De Roeck G 2001 One-year monitoring of the Z24-Bridge: environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30(2): 149–171

    Article  Google Scholar 

  26. Sohn H and Farrar C R 2001 Damage diagnosis using time series analysis of vibration signals. Smart Mater. Struct. 10(3): 446–451

    Article  Google Scholar 

  27. Zhang Q W 2007 Statistical damage identification for bridges using ambient vibration data. Comput. Struct. 85(7–8): 476–485

    Article  Google Scholar 

  28. Samuel da Silva, Júnior M D and Junior V L 2007 Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition. J. Braz. Soc. Mech. Sci. Eng. 29(2): 174–184

    Article  Google Scholar 

  29. Lakshmi K and Rama Mohan Rao A 2015 Damage identification technique based on time series models for LANL and ASCE benchmark structures. J. Non Destr. Test. Cond. Monit. 57(10): 580–588

    Article  Google Scholar 

  30. Nair K K, Kiremidjian A S and Law K H 2006 Time series based damage detection and localization algorithm with application to the ASCE benchmark structure. J. Sound Vib. 291(1–2): 349–368. https://doi.org/10.1016/j.jsv.2005.06.016

    Article  Google Scholar 

  31. Zheng H and Mita A 2008 Damage indicator defined as the distance between ARMA models for structural health monitoring. Struct. Control Health Monit. 15(7): 992–1005

    Article  Google Scholar 

  32. Carden E P and Brownjohn J M W 2008 ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mech. Syst. Signal Process. 22(2): 295–314

    Article  Google Scholar 

  33. Samuel da Silva, Júnior M D, Junior V L and Brennan M J 2008 Structural damage detection by fuzzy clustering. Mech. Syst. Signal Process. 22(7): 1636–1649

    Article  Google Scholar 

  34. Omenzetter P and Brownjohn J M W 2006 Application of time series analysis for bridge monitoring. Smart Mater. Struct. 15(1): 129–138. https://doi.org/10.1088/0964-1726/15/1/041

    Article  Google Scholar 

  35. Xing Z and Mita A 2012 A substructure approach to local damage detection of shear structure. Struct. Control Health Monit. 19(2): 309–318

    Article  Google Scholar 

  36. Lakshmi K and Rama Mohan Rao A 2016 Structural damage detection using ARMAX time series models and cepstral distances. Sādhanā 41(9): 1081–1097

    MathSciNet  MATH  Google Scholar 

  37. Mei L, Mita A and Zhou J 2016 An improved substructural damage detection approach of shear structure based on ARMAX model residual. Struct. Control Health Monit. 23(2): 218–236. https://doi.org/10.1002/stc.1766

    Article  Google Scholar 

  38. Mosavi A A, Dickey D, Seracino R and Rizkalla S 2012 Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mech. Syst. Signal Process. 26: 254–267. https://doi.org/10.1016/j.ymssp.2011.06.009

    Article  Google Scholar 

  39. Mattson S G and Pandit S M 2006 Statistical moments of autoregressive model residuals for damage localization. Mech. Syst. Signal Process. 20(3): 627–645. https://doi.org/10.1016/j.ymssp.2004.08.005

    Article  Google Scholar 

  40. Lakshmi K and Rama Mohan Rao A 2014 A robust damage-detection technique with environmental variability combining time-series models with principal components. Non Destr. Test. Eval. 29(4): 357–376

    Article  Google Scholar 

  41. Tejasree G, Lakshmi K and Rama Mohan Rao A 2015 Comparision of damage indices employed for structural damage detection using time series analysis. In: International Conference on Sustainable Energy and Built Environment, VIT-Chennai, pp. 424–430

  42. Ljung L 1998 System identification. In Signal analysis and prediction, pp 163–173, Birkhäuser, Boston, MA

    Book  Google Scholar 

  43. Prawin J, Rama Mohan Rao A and Lakshmi K 2016 Nonlinear parametric identification strategy combining reverse path and hybrid dynamic quantum particle swarm optimization. Nonlinear Dyn. 84(2): 797–815

    Article  MathSciNet  Google Scholar 

  44. Rama Mohan Rao A and Lakshmi K 2015 Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO. Meccanica 50(6): 1551–1578

    Article  MathSciNet  MATH  Google Scholar 

  45. Mallardo V and Alibadi M H 2013 Optimal sensor placement for structural damage and impact identification: a review. Struct. Durab. Health Monit. 9: 287–323

    Google Scholar 

  46. Kammer D C 2005 Sensor set expansion for modal vibration testing. Mech. Syst. Signal Process. 19(4): 700–713

    Article  Google Scholar 

  47. Rama Mohan Rao A and Anandakumar G 2008 Optimal sensor placement techniques for system identification and health monitoring of civil structures. Smart Struct. Syst. 4(4): 465–492

    Article  Google Scholar 

  48. http://institute.lanl.gov/ei/software-and-data

Download references

Acknowledgement

This paper is being published with the permission of the Director, CSIR-Structural Engineering Research Centre (SERC), Chennai.

Author information

Authors and Affiliations

  1. Academy of Scientific and Innovative Research, CSIR-Structural Engineering Research Centre, Chennai, 600113, India

    K Lakshmi & A Rama Mohan Rao

Authors
  1. K Lakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A Rama Mohan Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K Lakshmi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lakshmi, K., Rama Mohan Rao, A. Output-only damage localization technique using time series model. Sādhanā 43, 147 (2018). https://doi.org/10.1007/s12046-018-0912-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-018-0912-0

Keywords

Search

Navigation