Skip to main content
Log in

Health monitoring of timber poles using time–frequency analysis techniques and stress wave propagation

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

Abstract

Stress wave propagation can be effectively used as a non-destructive testing technique for the condition assessment of timber utility poles. Stress waves can be generated by applying a transverse impact close to the ground level of the pole, within the comfortable reaching height of the inspectors. The material behaviour of timber, the presence of natural imperfections and soil-pole interaction generate complexity in the propagation and reflection of generated transverse stress wave. Furthermore, the nonstationary nature of the reflected stress wave creates difficulties in identifying the defect features (i.e., location and severity of the defect) in the time domain. However, the time–frequency representation (TFR) can reveal local features of a reflected stress wave signal in both time and frequency domains simultaneously and hence, it can be effectively used to extract information to assess the health of timber poles. This paper presents a review of widely used TFR techniques in structural health monitoring and evaluates the applicability and efficiency of those methods in the context of structural health monitoring of timber utility poles. The signals collected from laboratory pole experiments and in-situ poles within power distribution networks along with numerical simulations were used to evaluate the performance of different TFR. A numerical simulation of the pole-soil system with different defect types and levels was carried out using the finite element method. Finally, the effect of different defect types, locations and applied impacts on the performance of different TFRs were evaluated.

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

Access this article

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
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Sriskantharajah B, Gad E, Bandara S, Rajeev P, Flatley I (2020) Condition assessment tool for timber utility poles using stress wave propagation technique. Nondestructive Testing Evaluation, 1–21.

  2. Wang JY, Stirling R, Morris PI, Taylor A, Lloyd J, Kirker G, ... Morrell JJ (2018) Durability of mass timber structures: a review of the biological risks. Wood Fiber Sci 110–127.

  3. Haldar A, Tucker K (2007) Condition based management of wood pole transmission lines using structural reliability analysis. In Electrical transmission line and substation structures: structural reliability in a changing world (pp 304–316)

  4. Shupe T, Lebow S, Ring D (2008) Causes and control of wood decay, degradation & stain. Pub.(Louisiana Cooperative Extension Service)-2703.[Baton Rouge, La.]: Louisiana State University Agricultural Center,[2008]. 26 pages., 2703

  5. Mudiyanselage SN, Rajeev P, Gad E, Sriskantharajah B, Flatley I (2020) Non-destructive techniques for condition assessment of timber utility poles. In: ACMSM25 (pp 941–951), Springer, Singapore.

  6. Subhani M, Li J, Samali B, Yan N (2013) Determination of the embedded lengths of electricity timber poles utilising flexural wave generated from impactí. Aust J Struct Eng 14(1):85–96

    Article  Google Scholar 

  7. Tanasoiu V, Miclea C, Tanasoiu C (2002) Nondestructive testing techniques and piezoelectric ultrasonics transducers for wood and built in wooden structures. J Optoelectron Adv Materials 4(4):949–957

    Google Scholar 

  8. Feng Q, Kong Q, Song G (2016) Damage detection of concrete piles subject to typical damage types based on stress wave measurement using embedded smart aggregates transducers. Measurement 88:345–352

    Article  Google Scholar 

  9. Ding X, Liu H, Liu J, Chen Y (2011) Wave propagation in a pipe pile for low-strain integrity testing. J Eng Mech 137(9):598–609

    Article  Google Scholar 

  10. Turner MJ (1997) Integrity testing in piling practice. CIRIA Report 144, p 336

  11. Li J, Subhani M, Samali B (2012) Determination of embedment depth of timber poles and piles using wavelet transform. Adv Struct Eng 15(5):759–770

    Article  Google Scholar 

  12. Bandara S, Rajeev P, Gad E, Sriskantharajah B (2020) Damage severity estimation of timber poles using stress wave propagation and wavelet entropy evolution. J Nondestruct Eval Diagn Progn Eng Syst 4(1)

  13. Sriskantharajah B (2015) Timber pole integrity testing, PhD thesis, Swinburne University of Technology, Victoria, Australia

  14. Bandara S, Rajeev P, Gad E, Sriskantharajah B, Flatley I (2019) Damage detection of in-service timber poles using Hilbert-Huang transform. NDT E Int 107:102141

    Article  Google Scholar 

  15. Mudiyanselage S, Rajeev P, Gad E, Sriskantharajah B, Flatley I (2019) Application of stress wave propagation technique for condition assessment of timber poles. Struct Infrastructure Eng 15(9):1234–1246

    Article  Google Scholar 

  16. Liu F, Gao S, Tian Z, Liu D (2020) A new time-frequency analysis method based on state-space model and energy gridding for offshore wind turbines. Mar Struct 72:102782

    Article  Google Scholar 

  17. Marmolejo M, Marulanda J, Thomson P (2020) Time-scale analysis based modal identification using mobile sensors. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 36(1).

  18. Flandrin P (2018) Explorations in time-frequency analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  19. Boggiatto, P., Cappiello, M., Cordero, E., Coriasco, S., Garello, G., Oliaro, A., & Seiler, J. (2020). Advances in Microlocal and Time-Frequency Analysis. Springer International Publishing.

  20. Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Signal Process 38(1):165–205

    Article  Google Scholar 

  21. Addison PS (2017) The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC Press, Boca Raton

    Book  Google Scholar 

  22. Iatsenko D, McClintock PV, Stefanovska A (2015) Linear and synchrosqueezed time–frequency representations revisited: overview, standards of use, resolution, reconstruction, concentration, and algorithms. Digital Signal Process 42:1–26

    Article  MathSciNet  Google Scholar 

  23. Mateo C, Talavera JA (2018) Short-time Fourier transform with the window size fixed in the frequency domain. Digital Signal Process 77:13–21

    Article  MathSciNet  Google Scholar 

  24. Debnath L, Shah FA (2015) The Wigner-Ville distribution and time–frequency signal analysis. Wavelet Transforms and Their Applications. Birkhäuser, Boston, pp 287–336

    MATH  Google Scholar 

  25. Levy C, Pinchas M, Pinhasi Y (2018) A new approach for the characterization of nonstationary oscillators using the wigner-ville distribution. Math Probl Eng. https://doi.org/10.1155/2018/4942938

    Article  MathSciNet  MATH  Google Scholar 

  26. Born M, Jordan P (1925) Zur quantenmechanik. Zeitschrift für Physik 34(1):858–888

    Article  Google Scholar 

  27. Choi H-I, Williams WJ (1989) Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Trans Acoust Speech Signal Process 37(6):862–871

    Article  Google Scholar 

  28. Cohen L (1989) Time-frequency distributions-a review. Proc IEEE 77(7):941–981

    Article  Google Scholar 

  29. Auger F, Flandrin P (1995) Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans Signal Process 43(5):1068–1089

    Article  Google Scholar 

  30. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, ... Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971):903–995.

  31. Yu Y, Subhani M, Hoshyar AN, Li J, Li H (2020) Automated health condition diagnosis of in-situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework. Int J Struct Stab Dyn 2042002

  32. Dackermann U, Yu Y, Niederleithinger E, Li J, Wiggenhauser H (2017) Condition assessment of foundation piles and utility poles based on guided wave propagation using a network of tactile transducers and support vector machines. Sensors 17(12):2938

    Article  Google Scholar 

  33. Yu Y, Dackermann U, Li J, Niederleithinger E (2019) Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory. Struct Health Monitoring 18(1):123–142

    Article  Google Scholar 

  34. Yu Y, Subhani M, Dackermann U, Li J (2019) Novel hybrid method based on advanced signal processing and soft computing techniques for condition assessment of timber utility poles. J Aerospace Eng 32(4):04019032

    Article  Google Scholar 

  35. Bendat JS, Piersol AG (1980) Engineering applications of correlation and spectral analysis. Wiley-Interscience, New York, p 315

    MATH  Google Scholar 

  36. Douglas RA, Holt JD (1994) Determining length of installed timber pilings by dispersive wave propagation methods. Centre for Transportation Engineering, North Carolina State University

  37. Auger F, Flandrin P, Gonçalvès P, Lemoine O, (1996) Time-frequency toolbox. CNRS France-Rice University, 46.

  38. Haq M, Bhalla S, Naqvi T (2020) Fatigue damage monitoring of reinforced concrete frames using wavelet transform energy of pzt-based admittance signals. Measurement, 108033.

  39. Ganguli R (2020) Wavelet based damage detection. Structural health monitoring. Springer, Singapore, pp 161–192

    Chapter  Google Scholar 

  40. Akbari J, Ahmadifarid M, Kazemi Amiri A (2020) Multiple crack detection using wavelet transforms and energy signal techniques. Frattura ed Integrità Strutturale 14(52):269–280

    Article  Google Scholar 

  41. Cohen L (1966) Generalized phase-space distribution functions. J Math Phys 7(5):781–786

    Article  MathSciNet  Google Scholar 

  42. Loughlin PJ, Pitton JW, Atlas LE (1993) Bilinear time-frequency representations: new insights and properties. IEEE Trans Signal Process 41(2):750–767

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Rajeev.

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

Bandara, S., Rajeev, P., Gad, E. et al. Health monitoring of timber poles using time–frequency analysis techniques and stress wave propagation. J Civil Struct Health Monit 11, 85–103 (2021). https://doi.org/10.1007/s13349-020-00440-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-020-00440-1

Keywords

Navigation