Abstract
Composite structures are susceptible to sudden failures due their complex modes of failure. One of these modes is delamination, the detachment of the layers due to the rupture in the fiber-matrix interface. To avoid catastrophic failures, several Structural Health Monitoring techniques are employed. Damage indexes based on wavelet transform techniques are vastly explored and provide prominent results. However, they depend on user experience and are usually formulated by trial and error. The present study proposes a damage index to identify delaminations in a laminated composite beam, yet, the development is based on a well-defined methodology. The proposed damage index is composed of a weighted sum of discrete wavelet transform detail coefficients, obtained by applying the transform to the mode shapes of the structure. Mode shapes were obtained numerically and damage was simulated with a stiffness reduction in some elements of the model. A mixture design analysis and a multiobjective optimization were used for tuning the damage index parameters, which improved the accuracy of the damage index in 100% of the evaluated cases. The proposed method was capable of locating damage with substantial performance along the beam length and has the advantageous characteristic of being a no-baseline method.
Similar content being viewed by others
Abbreviations
- SHM:
-
Structural health monitoring
- WT:
-
Wavelet transform
- CWT:
-
Continuous wavelet transform
- DWT:
-
Discrete wavelet transform
- MOOP:
-
Multiobjective optimization problems
- RSM:
-
Response surface methodology
- MD:
-
Mixture design
- DI:
-
Damage index
- a :
-
Scaling factor for CWT
- b :
-
Shifting factor for CWT
- j :
-
Scaling factor for DWT
- k :
-
Scaling factor for DWT
- \(\psi (t)\) :
-
Wavelet function
- s(t):
-
Generic signal
- \(x_{i}\) :
-
Mixture design components
- p :
-
Total mixture design components
- m :
-
Mixture design polynomial degree
- n :
-
Number of mode shapes
- N :
-
Total of points generated in mixture design
- K :
-
Stiffness matrix
- \(E_{1}\) :
-
Young’s modulus in longitudinal direction
- \(E_{2}\) :
-
Young’s modulus in lateral direction
- \(G_{12}\) :
-
In-plane shear modulus
- \(\nu _{12}\) :
-
Poisson’s ratio
- \(\rho \) :
-
Density
- \(\alpha \) :
-
Stiffness multiplier
- \(\omega _n\) :
-
Natural frequency
References
Yang G, Park M, Park S-J (2019) Recent progresses of fabrication and characterization of fibers-reinforced composites: a review. Compos Commun 14:34–42
Le MQ, Bainier H, Néron D, Ha-Minh C, Ladevèze P (2018) On matrix cracking and splits modeling in laminated composites. Compos Part A Appl Sci Manuf 115:294–301
Gomes GF, Mendéz YAD, da Alexandrino PSL, da Cunha Jr SS, Ancelotti AC Jr (2018) The use of intelligent computational tools for damage detection and identification with an emphasis on composites-a review. Compos Struct 196:44–54
Katunin A, dos Santos JVA, Lopes H (2019) Application of wavelet analysis to differences in modal rotations for damage identification. In: IOP conference series: materials science and engineering, vol 561, IOP Publishing, p 012024
Zhu L-F, Ke L-L, Zhu X-Q, Xiang Y, Wang Y-S (2019) Crack identification of functionally graded beams using continuous wavelet transform. Compos Struct 210:473–485
Abdulkareem M, Ganiyu A, Majid MZA (2019) Damage identification in plate using wavelet transform and artificial neural network. In: IOP conference series: materials science and engineering, vol 513, IOP Publishing, p 012015
Ashory M-R, Ghasemi-Ghalebahman A, Kokabi M-J (2016) Damage detection in laminated composite plates via an optimal wavelet selection criterion. J Reinf Plast Compos 35(24):1761–1775
Sha G, Radzienski M, Soman R, Cao M, Ostachowicz W, Xu W (2020) Multiple damage detection in laminated composite beams by data fusion of teager energy operator-wavelet transform mode shapes. Compos Struct 235:111798
Janeliukstis R, Rucevskis S, Akishin P, Chate A (2016) Wavelet transform based damage detection in a plate structure. Proc Eng 161:127–132
Yang C, Oyadiji SO (2017) Damage detection using modal frequency curve and squared residual wavelet coefficients-based damage indicator. Mech Syst Signal Process 83:385–405
Zhou J, Li Z, Chen J (2018) Damage identification method based on continuous wavelet transform and mode shapes for composite laminates with cutouts. Compos Struct 191:12–23
Xu W, Ding K, Liu J, Cao M, Radzieński M, Ostachowicz W (2019) Non-uniform crack identification in plate-like structures using wavelet 2d modal curvature under noisy conditions. Mech Syst Signal Process 126:469–489
dos Santos AJV, Katunin A, Lopes H (2019) Vibration-based damage identification using wavelet transform and a numerical model of shearography. Int J Struct Stab Dyn 19(04):1950038
Abdulkareem M, Bakhary N, Vafaei M, Noor NM, Mohamed RN (2019) Application of two-dimensional wavelet transform to detect damage in steel plate structures. Measurement 146:912–923
Pereira JJ, Chuman M, Cunha Jr SS, Gomes GF (2020) Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures. Eng Comput. https://www.emerald.com/insight/content/doi/10.1108/EC-12-2019-0564/full/html
Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (sfo) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626
Gomes GF, de Almeida FA, Junqueira DM, da Cunha Jr SS, Ancelotti AC Jr (2019) Optimized damage identification in cfrp plates by reduced mode shapes and ga-ann methods. Eng Struct 181:111–123
Ghannadi P, Kourehli SS, Noori M, Altabey WA (2020) Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes. Adv Struct Eng 23(13):2850–2865. https://journals.sagepub.com/doi/abs/10.1177/1369433220921000
Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862
da Alexandrino PSL, Gomes GF, Cunha SS Jr (2020) A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making. Inverse Probl Sci Eng 28(1):21–46
Taha MMR, Noureldin A, Lucero JL, Baca TJ (2006) Wavelet transform for structural health monitoring: a compendium of uses and features. Struct Health Monit 5(3):267–295
Addison P (2017) The illustrated wavelet transform handbook, 2nd edn. CRC Press, Boca Raton
Mason RL, Gunst RF, Hess JL (2003) Statistical design and analysis of experiments: with applications to engineering and science, vol 474. Wiley, Hoboken
Montgomery DC (2017) Design and analysis of experiments. Wiley, Hoboken
Rao SS (2000) Engineering optimization: theory and practice. New Age International, New Delhi
Yang C, Oyadiji SO (2017) Delamination detection in composite laminate plates using 2d wavelet analysis of modal frequency surface. Comput Struct 179:109–126
Gomes GF, Mendéz YAD, da Cunha SS, Ancelotti AC (2018) A numerical-experimental study for structural damage detection in cfrp plates using remote vibration measurements. J Civ Struct Health Monit 8(1):33–47
Vafaei M, Alih SC, Rahman ABA, Adnan AB (2015) A wavelet-based technique for damage quantification via mode shape decomposition. Struct Infrastruct Eng 11(7):869–883
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197
Subashini G, Bhuvaneswari MC (2012) Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems. Sadhana 37(6):675–694
Yoon KP, Hwang C-L (1981) Multiple attribute decision making, methods and applications: lecture notes in economics and mathematical systems, vol 186. Springer, Berlin
Acknowledgements
The authors are grateful to the Brazilian Funding Institutions CAPES, CNPq (Grant number 431219/2018-4) and FAPEMIG (Grant number APQ-00385-18) for the financial supports.
Author information
Authors and Affiliations
Corresponding author
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.
Appendix A. Objective functions
Appendix A. Objective functions
The eighteen objective functions are listed below:
Rights and permissions
About this article
Cite this article
Oliver, G.A., Pereira, J.L.J., Francisco, M.B. et al. Parameter tuning for wavelet transform-based damage index using mixture design. Engineering with Computers 38 (Suppl 4), 3609–3630 (2022). https://doi.org/10.1007/s00366-021-01481-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-021-01481-w