Prediction of crack depth and position in vibrating beams using artificial neural networks

• Autorzy: Bouboulas Athanasios , Nikolakopoulos Pantelis , Anifantis Nikolaos

Identyfikatory

DOI

10.29354/diag/154758

• Języki publikacji: EN

Abstrakty

EN

The aim of this paper is to develop a finite element procedure for crack prediction in vibrating beams. Based on this procedure, full frictional contact conditions are introduced between the crack surfaces in order to consider the breathing of crack. The region surrounding the crack is simulated by two-dimensional finite elements. An incremental-iterative procedure is employed to solve the nonlinear dynamic equations governing this problem. The obtained time response is processed with Fast Fourier Transform to extract its frequency components. The first three natural frequencies are input to a trained Artificial Neural Network for depth and position prediction of the crack. This study is validated for a dynamic loading cantilever beam. It is found that the proposed procedure is capable of predicting the crack depth and position with high accuracy.

Słowa kluczowe

EN

crack prediction; contact conditions; finite element method; artificial neural networks

PL

pęknięcia; warunki kontaktu; metoda elementów skończonych; sztuczne sieci neuronowe

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2022
• Tom: Vol. 23, No. 3
• Strony: art. no 2022307
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Bouboulas Athanasios

• Machine Design Laboratory, Mechanical and Aeronautics Engineering Department, University of Patras, Greece

autor

Nikolakopoulos Pantelis

• Machine Design Laboratory, Mechanical and Aeronautics Engineering Department, University of Patras, Greece

autor

Anifantis Nikolaos

• Machine Design Laboratory, Mechanical and Aeronautics Engineering Department, University of Patras, Greece

Bibliografia

• 1. Montalvão E, Silva JM, Aráujo Gomes AJM. Experimental dynamic analysis of cracked free-free beams. Experimental Mechanics 1990; 30:20-25. https://doi.org/10.1007/BF02322697.
• 2. Doebling SW, Farrar CR, Prime MB. A summary review of vibration-based damage identification methods. Shock and Vibration Digest 1998; 30 (2):91-105.
• 3. Wu X, Ghaboussi J, Garrett JH. Use of neural networks in detection of structural damage. Computers & Structures 1992;42(4):649-659. https://doi.org/10.1016/0045-7949(92)90132-J.
• 4. Nazari F, Abolbashari MH. Double cracks identification in functionally graded beams using artificial neural network. Journal of Solid Mechanics 2013; 5(1):14-21.
• 5. Aydin K, Kisi O. Damage diagnosis in beam-like structures by artificial neural networks. Journal of Civil Engineering and Management 2015; 21(5):591-604. https://doi.org/10.3846/13923730.2014.890663.
• 6. Gowd BP, Jayasree K., Hegde MN. Comparison of artificial neural networks and fuzzy logic approaches for crack detection in a beam like structure. International Journal of Artificial Intelligence and Applications 2018;9(1):35-52. https://doi.org/10.5121/ijaia.2018.9103.
• 7. Kekan AH, Kumar BR. Crack depth and crack location identification using artificial neural network. International Journal of Mechanical Production Engineering Research and Development 2019; 9(2): 699-708.
• 8. Maurya M, Sadarang J, Panigrahi I. Detection of crack in structure using dynamic analysis and artificial neural network. Engineering Solid Mechanics 2020; 8(3): 285-300. https://doi.org/10.5267/j.esm.2019.11.002.
• 9. Gudmunson P. The dynamic behavior of slender structures with cross-sectional cracks. Journal of the Mechanics and Physics of Solids 1983; 31(4):329-345. https://doi.org/10.1016/0022-5096(83)90003-0.
• 10. Shen MH, Chu YC. Vibrations of beams with a fatigue crack. Computers and Structures 1992; 45(1):79-93. https://doi.org/10.1016/0045-7949(92)90347-3.
• 11. Bouboulas AS, Anifantis NK. Finite element modeling of a vibrating beam with a breathing crack: observations on crack detection. Structural Health Monitoring 2011; 10(2):131-145. https://doi.org/10.1177%2F1475921710373286.
• 12. Ma H, Zeng J, Lang Z, Zhang L, Guo Y, Wen B. Analysis of the dynamic characteristics of a slantcracked cantilever beam. Mechanical Systems and Signal Processing 2016; 75, 261-279. https://doi.org/10.1016/j.ymssp.2015.12.009.
• 13. Long H, Liu Y, Liu K. Nonlinear Vibration Analysis of a Beam with a Breathing Crack. Applied Sciences 2019;9(18):3874. https://doi.org/10.3390/app9183874.
• 14. Bathe, K.J. Finite Element Procedures. Prentice-Hall, Upper Saddle River, NJ, 1996.
• 15. Brigham EO. The Fast Fourier and Applications. Englewood Cliffs, NJ: Prentice Hall, 1988.
• 16. McCarthy J. Ascribing mental qualities to machines. In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence, Humanities Press, 1979.
• 17. Marquardt DW. An algorithm for least squares estimation of non-linear parameters. Journal of the Society for Industrial and Applied Mathematics 1963; 11(2):431-441. https://doi.org/10.1137/0111030.
• 18. Hagan MT, Menhaj MB. Training feed forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 1994; 5(6):889-993. https://doi.org/10.1109/72.329697.
• 19. Dimarogonas AD. Vibration of cracked structures: A state of the art review. Engineering Fracture Mechanics 1996; 55(5):831-857. https://doi.org/10.1016/0013-7944(94)00175-8.
• 20. Nandwana BP, Maiti SK. Modelling of vibration of beam in presence of inclined edge or internal crack for its possible detection based on frequency measurements. Engineering Fracture Mechanics 1997; 58(3):193-205. https://doi.org/10.1016/S0013-7944(97)00078-7.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).