Skip to main content
SpringerLink
Log in

Optimising Hyperparameters of Artificial Neural Network Topology for SHM Damage Detection and Identification

  • Original Research Article
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

Artificial neural networks (ANNs) are a powerful method for solving classification problems, particularly for clustered data. However, one of the main challenges in using ANNs for general classification problems is determining the best topology (number of layers and neurons per layer) for a given problem or dataset. This study proposes a novel approach to address this challenge by using a optimisation algorithm. The algorithm first solves an external optimisation problem to obtain the optimal topology that maximises the accuracy of supervised learning. After, an inner optimisation problem is used to train the network to detect damage, using the Frequency Response Function of vibration-based measurements as input. To test the proposed methodology, it is applied to three different mechanical systems: metallic beams, rolling bearings, and composite plates. Principal component analysis is used to reduce the number of inputs to the neural network, and the external optimisation problem is solved using Particle swarm optimisation. The results demonstrate that the proposed methodology can accurately assess damage. Through the results, a discussion is presented about the potentialities and limitations of the proposed methodology as a support tool for damage detection.

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

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon request.

References

  1. S.S. Kessler, S.M. Spearing, M.J. Atalla, C.E. Cesnik, C. Soutis, Damage detection in composite materials using frequency response methods. Compos. B Eng. 33(1), 87–95 (2002). https://doi.org/10.1016/S1359-8368(01)00050-6

    Article  Google Scholar 

  2. C.R. Farrar, K. Worden, An introduction to structural health monitoring. Philos. Trans. R. Soc. Lond. A: Mat. Phys. Eng. Sci. 365(1851), 303–315 (2007). https://doi.org/10.1098/rsta.2006.1928

    Article  Google Scholar 

  3. S.A. Rahim, G. Manson, Kernel principal component analysis for structural health monitoring and damage detection of an engineering structure under operational loading variations. J. Fail. Anal. Prev. 21, 1981–1990 (2021). https://doi.org/10.1007/s11668-021-01260-1

    Article  Google Scholar 

  4. A.R. Webb, K.D. Copsey, G. Cawley, Statistical Pattern Recognition, vol. 2. Wiley Online Library, The Atrium, Southern Gate, Chichester (2011). https://doi.org/10.1002/9781119952954

  5. P. Jayaswal, A. Wadhwani, Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review. Aust. J. Mech. Eng. 7(2), 157–171 (2009). https://doi.org/10.1080/14484846.2009.11464588

    Article  Google Scholar 

  6. Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, A.K. Nandi, Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process. 138, 106587 (2020). https://doi.org/10.1016/j.ymssp.2019.106587

    Article  Google Scholar 

  7. O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, D.J. Inman, A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech. Syst. Signal Process. 147, 107077 (2021). https://doi.org/10.1016/j.ymssp.2020.107077

    Article  Google Scholar 

  8. I.Y. Tumer, E.M. Huff, Principal components analysis of triaxial vibration data from helicopter transmissions, in 56th Meeting of the Society for Machinery Failure Prevention Technology (2001)

  9. C. Kao, S.-L. Hung, Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Comput. Struct. 81(28–29), 2631–2644 (2003). https://doi.org/10.1016/S0045-7949(03)00323-7

    Article  Google Scholar 

  10. P. Jayaswal, S. Verma, Application of vibration signature analysis techniques for rolling element bearing fault identification. Aust. J. Mech. Eng. 8(1), 21–36 (2011). https://doi.org/10.1080/14484846.2011.11464592

    Article  Google Scholar 

  11. O. Abdeljaber, O. Avci, S. Kiranyaz, M. Gabbouj, D.J. Inman, Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017). https://doi.org/10.1016/j.jsv.2016.10.043

    Article  Google Scholar 

  12. L.A. Pinedo-Sanchez, D.A. Mercado-Ravell, C.A. Carballo-Monsivais, Vibration analysis in bearings for failure prevention using CNN. J. Braz. Soc. Mech. Sci. Eng. 42(12), 1–17 (2020). https://doi.org/10.1007/s40430-020-02711-w

    Article  Google Scholar 

  13. P.A. Reis, K.M. Iwasaki, L.R. Voltz, E.L. Cardoso, R.D. Medeiros, Damage detection of composite beams using vibration response and artificial neural networks. Proc. Inst. Mech. Eng. Part L: J. Mater.: Des. Appl. 236(7), 1419–1430 (2022)

    CAS  Google Scholar 

  14. J. Li, U. Dackermann, Y.-L. Xu, B. Samali, Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles. Struct. Control Health Monit. 18(2), 207–226 (2011). https://doi.org/10.1002/stc.369

    Article  Google Scholar 

  15. Y. Ni, X. Zhou, J. Ko, Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks. J. Sound Vib. 290(1–2), 242–263 (2006). https://doi.org/10.1016/j.jsv.2005.03.016

    Article  Google Scholar 

  16. B. Samali, U. Dackermann, J. Li, Location and severity identification of notch-type damage in a two-storey steel framed structure utilising frequency response functions and artificial neural network. Adv. Struct. Eng. 15(5), 743–757 (2012). https://doi.org/10.1260/1369-4332.15.5.74

    Article  Google Scholar 

  17. R.P. Bandara, T.H. Chan, D.P. Thambiratnam, Frequency response function based damage identification using principal component analysis and pattern recognition technique. Eng. Struct. 66, 116–128 (2014). https://doi.org/10.1016/j.engstruct.2014.01.044

    Article  Google Scholar 

  18. V.M. Nistane, Wavelet-based features for prognosis of degradation in rolling element bearing with non-linear autoregressive neural network. Aust. J. Mech. Eng. 19(4), 423–437 (2021). https://doi.org/10.1080/14484846.2019.1630949

    Article  Google Scholar 

  19. G.F. Gomes, C.A. Diniz, S.S. Cunha, A.C. Ancelotti, Design optimization of composite prosthetic tubes using GA-ANN algorithm considering Tsai-Wu failure criteria. J. Fail. Anal. Prev. 17, 740–749 (2017). https://doi.org/10.1007/s11668-017-0304-5

    Article  Google Scholar 

  20. C. Zang, M. Imregun, Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. J. Sound Vib. 242(5), 813–827 (2001). https://doi.org/10.1006/jsvi.2000.3390

    Article  Google Scholar 

  21. U. Dackermann, J. Li, B. Samali, Dynamic-based damage identification using neural network ensembles and damage index method. Adv. Struct. Eng. 13(6), 1001–1016 (2016). https://doi.org/10.1260/1369-4332.13.6.10

    Article  Google Scholar 

  22. P. Selva, O. Cherrier, V. Budinger, F. Lachaud, J. Morlier, Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks. Eng. Struct. 56, 794–804 (2013). https://doi.org/10.1016/j.engstruct.2013.05.025

    Article  Google Scholar 

  23. P. Cawley, The impedance method of non-destructive inspection. NDT Int. 17(2), 59–65 (1984). https://doi.org/10.1016/0308-9126(84)90045-2

    Article  Google Scholar 

  24. R.J. O’Brien, J.M. Fontana, N. Ponso, L. Molisani, A pattern recognition system based on acoustic signals for fault detection on composite materials. Eur. J. Mech.-A/Solids. 64, 1–10 (2017). https://doi.org/10.1016/j.euromechsol.2017.01.007

    Article  Google Scholar 

  25. Z. Su, L. Ye, Quantitative damage prediction for composite laminates based on wave propagation and artificial neural networks. Struct. Health Monit. 4(1), 57–66 (2005). https://doi.org/10.1177/1475921705049747

    Article  Google Scholar 

  26. R.P. Bandara, T.H. Chan, D.P. Thambiratnam, Structural damage detection method using frequency response functions. Struct. Health Monit. 13(4), 418–429 (2014). https://doi.org/10.1177/1475921714522847

    Article  Google Scholar 

  27. D.B. Verstraete, E.L. Droguett, V. Meruane, M. Modarres, A. Ferrada, Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. Struct. Health Monit. 19(2), 390–411 (2020). https://doi.org/10.1177/1475921719850576

    Article  Google Scholar 

  28. N. Zobeiry, J. Reiner, R. Vaziri, Theory-guided machine learning for damage characterization of composites. Compos. Struct. 246, 112407 (2020). https://doi.org/10.1016/j.compstruct.2020.112407

    Article  Google Scholar 

  29. G. Puscasu, B. Codres, Nonlinear system identification and control based on modular neural networks. Int. J. Neural Syst. 21(04), 319–334 (2011). https://doi.org/10.1142/S0129065711002869

    Article  PubMed  Google Scholar 

  30. S.L.J. Ma, H. Hao, S. Jiang, Structural response recovery based on improved multi-scale principal component analysis considering sensor performance degradation. Adv. Struct. Eng. 21(2), 241–255 (2017). https://doi.org/10.1177/1369433217717114

    Article  Google Scholar 

  31. K. Ghoulem, T. Kormi, N. Bel Hadj Ali, Damage detection in nonlinear civil structures using kernel principal component analysis. Adv. Struct. Eng. 23(11), 2414–2430 (2020). https://doi.org/10.1177/1369433220913207

    Article  Google Scholar 

  32. V. Meruane, J. Mahu, Real-time structural damage assessment using artificial neural networks and antiresonant frequencies. Shock Vib. 2014, 1–14 (2010). https://doi.org/10.1155/2014/653279

    Article  Google Scholar 

  33. J.C.S. Queiroz, Y.T.B. Santos, I.C. Silva, C.T.T. Farias, Damage detection in composite materials using tap test technique and neural networks. J. Nondestr. Eval. 40, 27 (2021). https://doi.org/10.1007/s10921-021-00759-9

    Article  Google Scholar 

  34. M.J. Fogaça, E.L. Cardoso, R. Medeiros, Artificial neural networks to damage detection of composite materials, in Proceedings of the 26th International Congress of Mechanical Engineering (2021)

  35. M.J. Fogaça, E.L. Cardoso, R. Medeiros, A systematic approach to find the hyperparameters of artificial neural networks applied to damage detection in composite materials. J. Braz. Soc. Mech. Sci. Eng. 45(9), 496 (2023). https://doi.org/10.1007/s40430-023-04371-y

    Article  Google Scholar 

  36. I.H. Witten, E. Frank, M.A. Hall (ed.), Data Mining: Practical Machine Learning Tools and Techniques The Morgan Kaufmann Series in Data Management Systems, (Morgan Kaufmann, Boston, 2011), p.629. https://doi.org/10.1016/C2009-0-19715-5

    Book  Google Scholar 

  37. C.M. Bishop, Neural Networks for Pattern Recognition. (Oxford University Press, Birmingham, 1995)

    Book  Google Scholar 

  38. I.T. Jolliffe, J. Cadima, Principal component analysis: A review and recent developments. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2065), 20150202 (2016). https://doi.org/10.1098/rsta.2015.0202

    Article  Google Scholar 

  39. Z. Jaade, A., step-by-step explanation of principal component analysis (pca). https://builtin.com/data-science/step-step-explanation-principal-component-analysis. Accessed 17 May 2022 (2021)

  40. M.J. Powell, A view of algorithms for optimization without derivatives. Math. Today-Bull. Inst. Math. Appl. 43(5), 170–174 (2007)

    Google Scholar 

  41. R. Mohapatra, S. Saha, C.A.C. Coello, A. Bhattacharya, S.S. Dhavala, S. Saha, AdaSwarm: augmenting gradient-based optimizers in deep learning with swarm intelligence. IEEE Trans. Emerg. Top. Comput. Intell. (2020). https://doi.org/10.1109/TETCI.2021.3083428

    Article  Google Scholar 

  42. A. Banks, J. Vincent, C. Anyakoha, A review of particle swarm optimization. Part I: background and development. Nat. Comput. 6, 467–484 (2007). https://doi.org/10.1007/s11047-007-9049-5

    Article  Google Scholar 

  43. M.S. Innocente, J. Sienz, Coefficients’ settings in particle swarm optimization: insight and guidelines, in Computational Intelligence Techniques for Optimization and Data Modeling (B), vol. XXIX, pp. 9253–9269 (2010). https://doi.org/10.48550/arXiv.2101.11944

  44. T. Varga, A. Király, J. Abonyi, Improvement of PSO algorithm by memory based gradient search—application in inventory management. IEEE Trans. Emerg. Top. Comput. Intell. (2020). https://doi.org/10.1016/B978-0-12-405163-8.00019-3

    Article  Google Scholar 

  45. M. Malik, A. Arif, Ann prediction model for composite plates against low velocity impact loads using finite element analysis. Compos. Struct. 101, 290–300 (2013). https://doi.org/10.1016/j.compstruct.2013.02.020

    Article  Google Scholar 

  46. C.M. Bishop, N.M. Nasrabadi, Pattern Recognition and Machine Learning, vol 4 (Springer, New York, 2006)

    Google Scholar 

  47. N. Qian, On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999). https://doi.org/10.1016/S0893-6080(98)00116-6

    Article  CAS  PubMed  Google Scholar 

  48. J. Revels, M. Lubin, T. Papamarkou, Forward-mode automatic differentiation in Julia. https://doi.org/10.48550/arXiv.1607.07892. arXiv:1607.07892 (2016)

  49. O. Manzyuk, B.A. Pearlmutter, A.A. Radul, D.R. Rush, J.M. Siskind, Perturbation confusion in forward automatic differentiation of higher-order functions. J. Funct. Program. 29, 12 (2019). https://doi.org/10.1017/S095679681900008X

    Article  Google Scholar 

  50. I.B.V. Silva, P.J. Adeodato, Pca and gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets, in The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2664–2669 (IEEE, 2011). https://doi.org/10.1109/IJCNN.2011.6033567

  51. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    Google Scholar 

  52. S. Sengupta, S. Basak, R. Peters, Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 1(1), 157–191 (2018). https://doi.org/10.3390/make1010010

    Article  Google Scholar 

  53. R. Mendes, P. Cortez, M. Rocha, J. Neves, Particle swarms for feedforward neural network training, in Proceedings of the 2002 International Joint Conference on Neural Networks, 2002. IJCNN’02, vol. 2, pp. 1895–1899 (IEEE, 2002)

  54. L.R. Völtz, Fault diagnosis in composite structures using artificial neural network and principal component analysis. Master’s thesis, Santa Catarina State University - UDESC, Joinville, Santa Catarina, Brazil (2019)

  55. J. Bezanson, A. Edelman, S. Karpinski, V.B. Shah, Julia: a fresh approach to numerical computing. SIAM Rev. 59(1), 65–98 (2017)

    Article  Google Scholar 

  56. C.G. Looney, Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowl. Data Eng. 8(2), 211–226 (1996). https://doi.org/10.1109/69.494162

    Article  Google Scholar 

  57. I. Silva, D.H. Spatti, R.A. Flauzino, Artificial Neural Networks for Engineering and Applied Sciences. (Artliber, São Paulo, 2010) (in Portuguese)

    Google Scholar 

  58. M.S. Pereira, E.M. Bezerra, Utilization of neural networks in prediction of aeronautical compounds behaviour under shearing, in Meeting of Cientific Iniciation and Post-graduate Program of ITA, São José dos Campos (2007) (in Portuguese)

  59. R. De Medeiros, D. Vandepitte, V. Tita, Structural health monitoring for impact damaged composite: a new methodology based on a combination of techniques. Struct. Health Monit. 17(2), 185–200 (2018). https://doi.org/10.1177/147592171668844

    Article  Google Scholar 

  60. S.D. Sharma, Performance evaluation of the signal processing based transfer learning algorithm for the fault classification at different datasets. J. Fail. Anal. Prev. (2023). https://doi.org/10.1007/s11668-023-01648-1

    Article  Google Scholar 

  61. P. Gangsar, R. Tiwari, Multiclass fault taxonomy in rolling bearings at interpolated and extrapolated speeds based on time domain vibration data by svm algorithms. J. Fail. Anal. Prev. 14, 826–837 (2014). https://doi.org/10.1007/s11668-014-9893-4

    Article  Google Scholar 

Download references

Funding

The authors acknowledge the financial support of the Santa Catarina State Research and Innovation Funding Agency, Brazil (FAPESC process number: 2017TR1747, 2021TR843, 48/2021, 2023TR563), Coordination for the Improvement of the Higher Level Personnel, Brazil (CAPES Finance Code 001), and PROMOP (Programa de Bolsas de Monitoria de Pós-Graduação) of the Santa Catarina State University, Brazil. Ricardo De Medeiros acknowledges the financial support of the National Council for Scientific and Technological Development (CNPq process number: 304795/2022-4). Eduardo Lenz Cardoso acknowledges the financial support of the National Council for Scientific and Technological Development, Brazil (CNPq process number: 303900/2020-2). The authors would like to thank Navy Technological Centre (CTM - Brazil) for manufacturing the composite plates specimens. The authors would like to thank Julio Zago and Guilherme Zago for the data acquisition for the rolling bearings.

Author information

Author notes

  1. Luísa Rosenstock Völtz, Matheus Janczkowski Fogaça, Eduardo Lenz Cardoso and Ricardo De Medeiros have contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, Santa Catarina State University - UDESC, Rua Paulo Malschitzki, 200 – Zona Industrial Norte, 89219-710, Joinville, Santa Catarina, Brazil

    Luísa Rosenstock Völtz, Matheus Janczkowski Fogaça, Eduardo Lenz Cardoso & Ricardo De Medeiros

Authors
  1. Luísa Rosenstock Völtz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matheus Janczkowski Fogaça
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduardo Lenz Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ricardo De Medeiros
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo De Medeiros.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Rosenstock Völtz, L., Janczkowski Fogaça, M., Lenz Cardoso, E. et al. Optimising Hyperparameters of Artificial Neural Network Topology for SHM Damage Detection and Identification. J Fail. Anal. and Preven. 24, 955–975 (2024). https://doi.org/10.1007/s11668-024-01888-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-024-01888-9

Keywords

Search

Navigation