Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression
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Published
Jun 4, 2023
Myeongbaek Youn
Yunhan Kim
Dongki Lee
Minki Cho
Byeng D. Youn
Abstract
A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts, (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately.
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Keywords
fatigue crack growth, prediction, Estimation, support vector regression, Data Challenge
References
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Tipping, M. E. (2003). Bayesian inference: An introduction to principles and practice in machine learning. In Summer School on Machine Learning, 41-62. doi:10.1007/978-3-540-28650-9_3
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Walker, K. (1970). The effect of stress ratio during crack propagation and fatigue for 2024-T3 and 7075-T6 aluminum. In Effects of environment and complex load history on fatigue life: ASTM International. doi: 10.1520/STP32032S
Yang, Y., Ng, C.-T., & Kotousov, A. (2018). Influence of crack opening and incident wave angle on second harmonic generation of Lamb waves. Smart Materials and Structures, 27(5), 055013. doi: 10.1088/1361-665X/aab867
Agarwal, S., & Mitra, M. (2014). Lamb wave based automatic damage detection using matching pursuit and machine learning. Smart materials and structures, 23(8), 085012. doi10.1088/0964-1726/23/8/085012
Ahmad, W., Khan, S. A., Islam, M. M., & Kim, J.-M. (2019). A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliability Engineering and System Safety, 184, 67-76. doi:10.1016/j.ress.2018.02.003
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751-1760. doi:10.1016/j.engappai.2013.02.006
Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
Bozchalooi, I. S., & Liang, M. (2008). A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals. Mechanical Systems and Signal Processing, 22(4), 915-933. doi:10.1016/j.ymssp.2007.10.006
Brownjohn, J. M. (2006). Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A: Mathematical, Physical Engineering Sciences, 365(1851), 589-622. doi:10.1098/rsta.2006.1925
Coelho, C. K., Das, S., Chattopadhyay, A., Papandreou-Suppappola, A., & Peralta, P. (2007). Detection of fatigue cracks and torque loss in bolted joints. In Health Monitoring of Structural and Biological Systems 2007, 6532, 653204. doi:10.1117/12.715984
Conn, A. R., Gould, N. I., & Toint, P. L. (2000). Trust region methods (Vol. 1): Siam.
Forman, R. G., Kearney, V., & Engle, R. (1967). Numerical analysis of crack propagation in cyclic-loaded structures. Journal of basic engineering, 89(3), 459-463. doi:10.1115/1.3609637
Gilks, W. R., Richardson, S., & Spiegelhalter, D. (1995). Markov chain Monte Carlo in practice: Chapman and Hall/CRC.
Haario, H., Laine, M., Mira, A., & Saksman, E. (2006). DRAM: efficient adaptive MCMC. Statistics and computing, 16(4), 339-354. doi:10.1007/s11222-006-9438-0
Janapati, V., Kopsaftopoulos, F., Li, F., Lee, S. J., & Chang, F. K. (2016). Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques. Structural Health Monitoring, 15(2), 143-161. doi: 10.1177/1475921715627490
Kessler, S. S., Spearing, S. M., & Soutis, C. (2002). Damage detection in composite materials using Lamb wave methods. Smart materials and structures, 11(2), 269. doi:10.1088/0964-1726/11/2/310
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314-334. doi:10.1016/j.ymssp.2013.06.004
Liu, M., Frangopol, D. M., & Kwon, K. (2010). Fatigue reliability assessment of retrofitted steel bridges integrating monitored data. Structural Safety, 32(1), 77-89. doi:10.1016/j.strusafe.2009.08.003
Neerukatti, R. K., Hensberry, K., Kovvali, N., & Chattopadhyay, A. (2016). A novel probabilistic approach for damage localization and prognosis including temperature compensation. Journal of Intelligent Material Systems and Structures, 27(5), 592-607. doi:10.1177/1045389X15575084
Oppenheim, A. V. (1999). Discrete-time signal processing: Pearson Education India.
Ostachowicz, W., & Güemes, A. (2013). New trends in structural health monitoring (Vol. 542): Springer Science & Business Media.
Paris, P., & Erdogan, F. (1963). A critical analysis of crack propagation laws. Journal of basic engineering, 85(4), 528-533. doi:10.1115/1.3656900
Peng, T., Liu, Y., Saxena, A., & Goebel, K. (2015). In-situ fatigue life prognosis for composite laminates based on stiffness degradation. Composite Structures, 132, 155-165. doi:10.1016/j.compstruct.2015.05.006
Qiu, L., Liu, M., Qing, X., & Yuan, S. (2013). A quantitative multidamage monitoring method for large-scale complex composite. Structural Health Monitoring, 12(3), 183-196. doi:10.1177/1475921713479643
Salcedo-Sanz, S., Ortiz-Garcı, E. G., Pérez-Bellido, Á. M., Portilla-Figueras, A., & Prieto, L. (2011). Short term wind speed prediction based on evolutionary support vector regression algorithms. Expert Systems with Applications, 38(4), 4052-4057. doi:10.1016/j.eswa.2010.09.067
Staszewski, W., Mahzan, S., & Traynor, R. (2009). Health monitoring of aerospace composite structures–Active and passive approach. composites Science and Technology, 69(11-12), 1678-1685. doi:10.1016/j.compscitech.2008.09.034
Tinga, T., & Loendersloot, R. (2014). Aligning PHM, SHM and CBM by understanding the physical system failure behaviour, Proceedings of the European Conference of the Prognostics and Health Management Society, 162-171.
Tipping, M. E. (2003). Bayesian inference: An introduction to principles and practice in machine learning. In Summer School on Machine Learning, 41-62. doi:10.1007/978-3-540-28650-9_3
Walker, H. M. (1940). Degrees of freedom. Journal of Educational Psychology, 31(4), 253. doi:10.1037/h0054588
Walker, K. (1970). The effect of stress ratio during crack propagation and fatigue for 2024-T3 and 7075-T6 aluminum. In Effects of environment and complex load history on fatigue life: ASTM International. doi: 10.1520/STP32032S
Yang, Y., Ng, C.-T., & Kotousov, A. (2018). Influence of crack opening and incident wave angle on second harmonic generation of Lamb waves. Smart Materials and Structures, 27(5), 055013. doi: 10.1088/1361-665X/aab867
Section
Technical Papers