SpringerLink
Log in

Inspecting Spectral Centroid and Relative Power of Allocated Spectra Using Artificial Neural Network for Damage Diagnosis in Beam Structures Under Moving Loads

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

This work designs a procedure for structural damage assessment in beams under moving loads. The proposal is based on the respective alterations of hybrid vibrational factors with respect to the structural changes, whereby matching them for accurate predictions. These hybrid factors are extracted from the vibrational responses and then are employed for an artificial neural network (ANN) to assess the structural condition.

Methods

An experiment is deployed to generate a databank for evaluation as well as ANN training. The maximal overlap discrete wavelet transform (MODWT) and the fast Fourier transform (FFT) are incorporated to split original signals into several power bands, which helps to exploit characteristics of individual vibrational modes: centroids and comparative powers of allocated spectra. The extracted hybrid factors are then fed into an ANN to predict the damage condition of the beam. The training process of this ANN uses a databank of extracted features from measured signals.

Results

The paper successfully finds out the damage-sensitive feature through signal processing. Alternatively, the implemented experiment provides a rich databank for the ANN training, and the training performance reaches a high accuracy. Moreover, the testing results reveal the superior generalization of the ANN because of automatic predictions with high precision. This proves the contribution of the study that the exploited features are proper for structural assessment, and the trained ANN learns the correlation of vibration features and structural state well.

Conclusions

The automation and effectiveness of the proposed approach are endorsed, which has high applicability and accuracy for damage assessment in beams. The proposed method is mainly devoted to identifying, quantifying, and localizing damage.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

Data should be available via correspondence upon reasonable requests.

References

  1. Knitter-Piątkowska A, Dobrzycki A (2020) Application of wavelet transform to damage identification in the steel structure elements. Appl Sci. https://doi.org/10.3390/app10228198

    Article  Google Scholar 

  2. Cao J, Zhou Z, Liu Y (2022) Damage localization for prefabricated bridges group using the area-ratio of the strain time-history curve. Measurement 198:111172

    Article  Google Scholar 

  3. Yan A-M, Golinval J-C (2006) Null subspace-based damage detection of structures using vibration measurements. Mech Syst Signal Process 20(3):611–626

    Article  Google Scholar 

  4. Zhang K, Ma C, Xu Y, Chen P, Du J (2021) Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis. Measurement 172:108976

    Article  Google Scholar 

  5. Ding X, Li Q, Lin L, He Q, Shao Y (2019) Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis. Measurement 141:380–395

    Article  Google Scholar 

  6. Doebling SW, Farrar CR, Prime MB (1998) A summary review of vibration-based damage identification methods. Shock and vibration digest 30(2):91–105

    Article  Google Scholar 

  7. Duhamel P, Vetterli M (1990) Fast Fourier transforms: a tutorial review and a state of the art. Signal Process 19(4):259–299

    Article  MathSciNet  Google Scholar 

  8. Cawley P, Adams RD (1979) The location of defects in structures from measurements of natural frequencies. J Strain Anal Eng Des 14(2):49–57

    Article  Google Scholar 

  9. Ngo NK, Nguyen TQ, Vu TV, Nguyen-Xuan H (2020) An fast Fourier transform–based correlation coefficient approach for structural damage diagnosis. Structural Health Monitoring:1475921720949561

  10. Zhang F-L, Kim C-W, Goi Y (2021) Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty. Struct Control Health Monit 28(2):e2659

    Article  Google Scholar 

  11. Erazo K, Sen D, Nagarajaiah S, Sun L (2019) Vibration-based structural health monitoring under changing environmental conditions using Kalman filtering. Mech Syst Signal Process 117:1–15

    Article  Google Scholar 

  12. Pham-Bao T, Nguyen-Nhat T, Ngo-Kieu N (2022) A novel approach to investigate the mechanical properties of the material for bridge health monitoring using convolutional neural network. Struct Infrastruct Eng. https://doi.org/10.1080/15732479.2022.2127792

    Article  Google Scholar 

  13. Ngo-Kieu N, Nguyen-Da T, Pham-Bao T, Nguyen-Nhat T, Nguyen-Xuan H (2021) Deep learning-based signal processing for evaluating energy dispersal in bridge structures. J Zhejiang Univ-SCI A 22(8):672–680

    Article  Google Scholar 

  14. Nguyen TD, Nguyen TQ, Nhat TN, Nguyen-Xuan H, Ngo NK (2020) A novel approach based on viscoelastic parameters for bridge health monitoring: a case study of Saigon bridge in Ho Chi Minh City – Vietnam. Mech Syst Signal Process 141:106728

    Article  Google Scholar 

  15. Gurley K, Kareem A (1999) Applications of wavelet transforms in earthquake, wind and ocean engineering. Eng Struct 21(2):149–167

    Google Scholar 

  16. Mallat S (1999) A wavelet tour of signal processing. Elsevier, San Diego

    Google Scholar 

  17. Epp T, Cha Y-J (2017) Air-coupled impact-echo damage detection in reinforced concrete using wavelet transforms. Smart Mater Struct 26(2):025018

    Article  Google Scholar 

  18. Zhu XQ, Law SS (2006) Wavelet-based crack identification of bridge beam from operational deflection time history. Int J Solids Struct 43(7):2299–2317

    Article  Google Scholar 

  19. Cantero D, Basu B (2015) Railway infrastructure damage detection using wavelet transformed acceleration response of traversing vehicle. Struct Control Health Monit 22(1):62–70

    Article  Google Scholar 

  20. Hester D, González A (2012) A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle. Mech Syst Signal Process 28:145–166

    Article  Google Scholar 

  21. Beale C, Niezrecki C, Inalpolat M (2020) An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades. Mech Syst Signal Process 142:106754

    Article  Google Scholar 

  22. Cha Y-J, Wang Z (2017) Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Struct Health Monit 17(2):313–324

    Article  Google Scholar 

  23. Liu Z, Zhang L, Carrasco J (2020) Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method. Renewable Energy 146:99–110

    Article  Google Scholar 

  24. Pawlak Z (1922) Knitter-Piątkowska A (2018) Influence of the wavelet order on proper damage location in plate structures. AIP Conf Proc 1:130006

    Google Scholar 

  25. Barros J, Diego RI (2008) Analysis of harmonics in power systems using the Wavelet-Packet transform. IEEE Trans Instrum Meas 57(1):63–69

    Article  Google Scholar 

  26. Costa FB, Driesen J (2013) Assessment of voltage sag indices based on scaling and wavelet coefficient energy analysis. IEEE Trans Pow Deliv 28(1):336–346

    Article  Google Scholar 

  27. Paramasivam S, Pl SA, Sathyamoorthi P (2022) Maximal overlap discrete wavelet transform-based power trace alignment algorithm against random delay countermeasure. ETRI J 44(3):512–523

    Article  Google Scholar 

  28. **ao F, Lu T, Wu M, Ai Q (2020) Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance. IET Gener Transm Distrib 14(1):140–147

    Article  Google Scholar 

  29. Mansi SK, Vanraj DSS (2021) MODWT and VMD based intelligent gearbox early stage fault detection approach. J Fail Anal Prev 21(5):1821–1837

    Article  Google Scholar 

  30. Wang Z, Cha Y-J (2022) Unsupervised machine and deep learning methods for structural damage detection: a comparative study. Eng Rep. https://doi.org/10.1002/eng2.12551

    Article  Google Scholar 

  31. Tran-Ngoc H et al (2022) Damage assessment in structures using artificial neural network working and a hybrid stochastic optimization. Sci Rep 12(1):4958

    Article  Google Scholar 

  32. Tuan Hoang A et al (2021) A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustainable Energy Technol Assess 47:101416

    Article  Google Scholar 

  33. Movsessian A, García Cava D, Tcherniak D (2021) An artificial neural network methodology for damage detection: demonstration on an operating wind turbine blade. Mech Syst Signal Process 159:107766

    Article  Google Scholar 

  34. Nick H, Aziminejad A, Hamid Hosseini M, Laknejadi K (2021) Damage identification in steel girder bridges using modal strain energy-based damage index method and artificial neural network. Eng Fail Anal 119:105010

    Article  Google Scholar 

  35. Rodrigues DVQ, Zuo D, Li C (2022) A MODWT-based algorithm for the identification and removal of jumps/short-term distortions in displacement measurements used for structural health monitoring. IoT 3(1):60–72

    Article  Google Scholar 

  36. Boashash B (2015) Time-frequency signal analysis and processing: a comprehensive reference. Academic press, London

    Google Scholar 

  37. Newland DE (2005) An introduction to random vibrations, spectral & wavelet analysis. Dover Publications, New York

    Google Scholar 

  38. Bilello C, Bergman LA (2004) Vibration of damaged beams under a moving mass: theory and experimental validation. J Sound Vib 274(3):567–582

    Article  Google Scholar 

  39. Pham-Bao T, Ngo-Kieu N, Vuong-Cong L, Nguyen-Nhat T (2022) Energy dissipation-based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City. Vietnam Struct Control Health Monitoring 29(7):e2956

    Google Scholar 

  40. François S et al (2021) Stabil: an educational Matlab toolbox for static and dynamic structural analysis. Comput Appl Eng Educ 29(5):1372–1389

    Article  Google Scholar 

  41. Cao MS, Sha GG, Gao YF, Ostachowicz W (2017) Structural damage identification using dam**: a compendium of uses and features. Smart Mater Struct 26(4):043001

    Article  Google Scholar 

  42. Curadelli RO, Riera JD, Ambrosini D, Amani MG (2008) Damage detection by means of structural dam** identification. Eng Struct 30(12):3497–3504

    Article  Google Scholar 

  43. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5(6):989–993

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study.

Funding

This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2021-20-05.

Author information

Authors and Affiliations

  1. Laboratory of Applied Mechanics (LAM), Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Tam Nguyen-Nhat, Luan Vuong-Cong, Vien Le-Ngoc & Toan Pham-Bao

  2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam

    Tam Nguyen-Nhat, Luan Vuong-Cong, Vien Le-Ngoc & Toan Pham-Bao

Authors
  1. Tam Nguyen-Nhat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luan Vuong-Cong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vien Le-Ngoc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Toan Pham-Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toan Pham-Bao.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen-Nhat, T., Vuong-Cong, L., Le-Ngoc, V. et al. Inspecting Spectral Centroid and Relative Power of Allocated Spectra Using Artificial Neural Network for Damage Diagnosis in Beam Structures Under Moving Loads. J. Vib. Eng. Technol. 12, 4617–4635 (2024). https://doi.org/10.1007/s42417-023-01140-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-023-01140-y

Keywords

Search

Navigation